pubg-game-predicition.ipynb

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     "output_type": "stream",
     "text": [
      "/kaggle/input/pubg-games-dataset/PUBG_Game_Prediction_data.csv\n"
     ]
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   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "<center><font color= \"green\">PUBG Game Prediction</font></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f81a7a88",
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    "tags": []
   },
   "source": [
    "<center><img src  =\"https://media.giphy.com/media/zaUTLcbjhWpTrNYoFG/giphy.gif\"></center>"
   ]
  },
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    "tags": []
   },
   "source": [
    "<font color =\"green\">Importing Libraries<a class=\"anchor\" id=\"import\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c9ab1993",
   "metadata": {
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"]=(16,6)\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import catboost as cb\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b929acd8",
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    "tags": []
   },
   "source": [
    "<font color =\"green\">Reading Data<a class=\"anchor\" id=\"import\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a2b8ce59",
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "df=pd.read_csv('/kaggle/input/pubg-games-dataset/PUBG_Game_Prediction_data.csv')"
   ]
  },
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       "\n",
       "   damageDealt  DBNOs  headshotKills  heals  killPlace  ...  revives  \\\n",
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       "1        91.47      0              0      0         57  ...        0   \n",
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       "\n",
       "   walkDistance  weaponsAcquired  winPoints  winPlacePerc  \n",
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       "1       1434.00                5          0        0.6400  \n",
       "2        161.80                2          0        0.7755  \n",
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       "4         49.75                2          0        0.1875  \n",
       "\n",
       "[5 rows x 29 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
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    }
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   "source": [
    "df.head()"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4446966 entries, 0 to 4446965\n",
      "Data columns (total 29 columns):\n",
      " #   Column           Dtype  \n",
      "---  ------           -----  \n",
      " 0   Id               object \n",
      " 1   groupId          object \n",
      " 2   matchId          object \n",
      " 3   assists          int64  \n",
      " 4   boosts           int64  \n",
      " 5   damageDealt      float64\n",
      " 6   DBNOs            int64  \n",
      " 7   headshotKills    int64  \n",
      " 8   heals            int64  \n",
      " 9   killPlace        int64  \n",
      " 10  killPoints       int64  \n",
      " 11  kills            int64  \n",
      " 12  killStreaks      int64  \n",
      " 13  longestKill      float64\n",
      " 14  matchDuration    int64  \n",
      " 15  matchType        object \n",
      " 16  maxPlace         int64  \n",
      " 17  numGroups        int64  \n",
      " 18  rankPoints       int64  \n",
      " 19  revives          int64  \n",
      " 20  rideDistance     float64\n",
      " 21  roadKills        int64  \n",
      " 22  swimDistance     float64\n",
      " 23  teamKills        int64  \n",
      " 24  vehicleDestroys  int64  \n",
      " 25  walkDistance     float64\n",
      " 26  weaponsAcquired  int64  \n",
      " 27  winPoints        int64  \n",
      " 28  winPlacePerc     float64\n",
      "dtypes: float64(6), int64(19), object(4)\n",
      "memory usage: 983.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "(4446966, 29)"
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     },
     "execution_count": 6,
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   ],
   "source": [
    "df.shape"
   ]
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     "data": {
      "text/plain": [
       "Id                 0\n",
       "groupId            0\n",
       "matchId            0\n",
       "assists            0\n",
       "boosts             0\n",
       "damageDealt        0\n",
       "DBNOs              0\n",
       "headshotKills      0\n",
       "heals              0\n",
       "killPlace          0\n",
       "killPoints         0\n",
       "kills              0\n",
       "killStreaks        0\n",
       "longestKill        0\n",
       "matchDuration      0\n",
       "matchType          0\n",
       "maxPlace           0\n",
       "numGroups          0\n",
       "rankPoints         0\n",
       "revives            0\n",
       "rideDistance       0\n",
       "roadKills          0\n",
       "swimDistance       0\n",
       "teamKills          0\n",
       "vehicleDestroys    0\n",
       "walkDistance       0\n",
       "weaponsAcquired    0\n",
       "winPoints          0\n",
       "winPlacePerc       1\n",
       "dtype: int64"
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     "execution_count": 7,
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   "source": [
    "<font color =\"green\">Data Wrangling <a class=\"anchor\" id=\"import\"></a>"
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   "source": [
    "**Check for the rows with missing win prediciton value**"
   ]
  },
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       "                     Id         groupId         matchId  assists  boosts  \\\n",
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       "         rideDistance  roadKills  swimDistance  teamKills vehicleDestroys  \\\n",
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    "df[df['winPlacePerc'].isnull()]"
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   "source": [
    "df.drop(2744604,inplace=True)"
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   "source": [
    "**Understanding Player Distribution in game**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c8448c3d",
   "metadata": {
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   "source": [
    "# Prepare new parameter to count how many new players are in game\n",
    "df['PlayersJoined']=df.groupby('matchId')['matchId'].transform('count')    "
   ]
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       "               Id         groupId         matchId  assists  boosts  \\\n",
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       "4  315c96c26c9aac  de04010b3458dd  6dc8ff871e21e6        0       0   \n",
       "\n",
       "   damageDealt  DBNOs  headshotKills  heals  killPlace  ...  rideDistance  \\\n",
       "0         0.00      0              0      0         60  ...        0.0000   \n",
       "1        91.47      0              0      0         57  ...        0.0045   \n",
       "2        68.00      0              0      0         47  ...        0.0000   \n",
       "3        32.90      0              0      0         75  ...        0.0000   \n",
       "4       100.00      0              0      0         45  ...        0.0000   \n",
       "\n",
       "   roadKills  swimDistance  teamKills  vehicleDestroys walkDistance  \\\n",
       "0          0          0.00          0                0       244.80   \n",
       "1          0         11.04          0                0      1434.00   \n",
       "2          0          0.00          0                0       161.80   \n",
       "3          0          0.00          0                0       202.70   \n",
       "4          0          0.00          0                0        49.75   \n",
       "\n",
       "   weaponsAcquired  winPoints  winPlacePerc  PlayersJoined  \n",
       "0                1       1466        0.4444             96  \n",
       "1                5          0        0.6400             91  \n",
       "2                2          0        0.7755             98  \n",
       "3                3          0        0.1667             91  \n",
       "4                2          0        0.1875             97  \n",
       "\n",
       "[5 rows x 30 columns]"
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     },
     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
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  },
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    {
     "data": {
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iAAAAAJASEgEAAACAlJAIAAAAAKSERAAAAAAgJSQCAAAAACkhEQAAAABI/R/nGlpOXJmX7QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df[df['PlayersJoined']>=75]['PlayersJoined'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e227a8d1",
   "metadata": {
    "papermill": {
     "duration": 0.019763,
     "end_time": "2024-06-24T14:48:42.994895",
     "exception": false,
     "start_time": "2024-06-24T14:48:42.975132",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<font color =\"green\">Analysing data<a class=\"anchor\" id=\"import\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbaeb814",
   "metadata": {
    "papermill": {
     "duration": 0.0198,
     "end_time": "2024-06-24T14:48:43.034664",
     "exception": false,
     "start_time": "2024-06-24T14:48:43.014864",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Kills Without Moving ?**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b146c7e",
   "metadata": {
    "papermill": {
     "duration": 0.02085,
     "end_time": "2024-06-24T14:48:43.075959",
     "exception": false,
     "start_time": "2024-06-24T14:48:43.055109",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<center><img src  =\"https://i.gifer.com/g1OF.gif\"></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c44d3d96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:48:43.118226Z",
     "iopub.status.busy": "2024-06-24T14:48:43.117831Z",
     "iopub.status.idle": "2024-06-24T14:48:43.158200Z",
     "shell.execute_reply": "2024-06-24T14:48:43.156932Z"
    },
    "papermill": {
     "duration": 0.064858,
     "end_time": "2024-06-24T14:48:43.160867",
     "exception": false,
     "start_time": "2024-06-24T14:48:43.096009",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#prepare new data parameter to gather the total distance travelled\n",
    "df['totalDistance']=df['rideDistance'] +df['swimDistance']+df['walkDistance'] \n",
    "\n",
    "#Preparing the data parameter to check for anmoly detection \n",
    "# that the person has not moved but still manages to do the kills\n",
    "\n",
    "df['KillsWithoutMoving']=((df['kills']>0) & (df['totalDistance']==0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a301ea5e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:48:43.202792Z",
     "iopub.status.busy": "2024-06-24T14:48:43.202411Z",
     "iopub.status.idle": "2024-06-24T14:48:43.231608Z",
     "shell.execute_reply": "2024-06-24T14:48:43.230511Z"
    },
    "papermill": {
     "duration": 0.052999,
     "end_time": "2024-06-24T14:48:43.234094",
     "exception": false,
     "start_time": "2024-06-24T14:48:43.181095",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>groupId</th>\n",
       "      <th>matchId</th>\n",
       "      <th>assists</th>\n",
       "      <th>boosts</th>\n",
       "      <th>damageDealt</th>\n",
       "      <th>DBNOs</th>\n",
       "      <th>headshotKills</th>\n",
       "      <th>heals</th>\n",
       "      <th>killPlace</th>\n",
       "      <th>...</th>\n",
       "      <th>swimDistance</th>\n",
       "      <th>teamKills</th>\n",
       "      <th>vehicleDestroys</th>\n",
       "      <th>walkDistance</th>\n",
       "      <th>weaponsAcquired</th>\n",
       "      <th>winPoints</th>\n",
       "      <th>winPlacePerc</th>\n",
       "      <th>PlayersJoined</th>\n",
       "      <th>totalDistance</th>\n",
       "      <th>KillsWithoutMoving</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1824</th>\n",
       "      <td>b538d514ef2476</td>\n",
       "      <td>0eb2ce2f43f9d6</td>\n",
       "      <td>35e7d750e442e2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8571</td>\n",
       "      <td>58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6673</th>\n",
       "      <td>6d3a61da07b7cb</td>\n",
       "      <td>2d8119b1544f87</td>\n",
       "      <td>904cecf36217df</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>346.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6000</td>\n",
       "      <td>42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11892</th>\n",
       "      <td>550398a8f33db7</td>\n",
       "      <td>c3fd0e2abab0af</td>\n",
       "      <td>db6f6d1f0d4904</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8947</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14631</th>\n",
       "      <td>58d690ee461e9d</td>\n",
       "      <td>ea5b6630b33d67</td>\n",
       "      <td>dbf34301df5e53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>157.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15591</th>\n",
       "      <td>49b61fc963d632</td>\n",
       "      <td>0f5c5f19d9cc21</td>\n",
       "      <td>904cecf36217df</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Id         groupId         matchId  assists  boosts  \\\n",
       "1824   b538d514ef2476  0eb2ce2f43f9d6  35e7d750e442e2        0       0   \n",
       "6673   6d3a61da07b7cb  2d8119b1544f87  904cecf36217df        2       0   \n",
       "11892  550398a8f33db7  c3fd0e2abab0af  db6f6d1f0d4904        2       0   \n",
       "14631  58d690ee461e9d  ea5b6630b33d67  dbf34301df5e53        0       0   \n",
       "15591  49b61fc963d632  0f5c5f19d9cc21  904cecf36217df        0       0   \n",
       "\n",
       "       damageDealt  DBNOs  headshotKills  heals  killPlace  ...  swimDistance  \\\n",
       "1824         593.0      0              0      3         18  ...           0.0   \n",
       "6673         346.6      0              0      6         33  ...           0.0   \n",
       "11892       1750.0      0              4      5          3  ...           0.0   \n",
       "14631        157.8      0              0      0         69  ...           0.0   \n",
       "15591        100.0      0              1      0         37  ...           0.0   \n",
       "\n",
       "       teamKills  vehicleDestroys  walkDistance  weaponsAcquired winPoints  \\\n",
       "1824           0                0           0.0                8         0   \n",
       "6673           0                0           0.0               22         0   \n",
       "11892          0                0           0.0               13         0   \n",
       "14631          0                0           0.0                7      1500   \n",
       "15591          0                0           0.0               10         0   \n",
       "\n",
       "       winPlacePerc  PlayersJoined  totalDistance  KillsWithoutMoving  \n",
       "1824         0.8571             58            0.0                True  \n",
       "6673         0.6000             42            0.0                True  \n",
       "11892        0.8947             21            0.0                True  \n",
       "14631        0.0000             73            0.0                True  \n",
       "15591        0.3000             42            0.0                True  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we can see that there are\n",
    "df[df['KillsWithoutMoving']==True].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b54818e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:48:43.276200Z",
     "iopub.status.busy": "2024-06-24T14:48:43.275805Z",
     "iopub.status.idle": "2024-06-24T14:48:43.289758Z",
     "shell.execute_reply": "2024-06-24T14:48:43.288716Z"
    },
    "papermill": {
     "duration": 0.037681,
     "end_time": "2024-06-24T14:48:43.291995",
     "exception": false,
     "start_time": "2024-06-24T14:48:43.254314",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([   1824,    6673,   11892,   14631,   15591,   20881,   23298,   24640,\n",
       "         25659,   30079,\n",
       "       ...\n",
       "       4426500, 4429697, 4432954, 4436511, 4437516, 4440232, 4440898, 4440927,\n",
       "       4441511, 4446682],\n",
       "      dtype='int64', length=1535)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['KillsWithoutMoving']==True].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "db8c5f24",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:48:43.334723Z",
     "iopub.status.busy": "2024-06-24T14:48:43.334343Z",
     "iopub.status.idle": "2024-06-24T14:48:43.349119Z",
     "shell.execute_reply": "2024-06-24T14:48:43.348023Z"
    },
    "papermill": {
     "duration": 0.03901,
     "end_time": "2024-06-24T14:48:43.351465",
     "exception": false,
     "start_time": "2024-06-24T14:48:43.312455",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1535, 32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here \n",
    "df[df['KillsWithoutMoving']==True].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bc313cfb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:48:43.394911Z",
     "iopub.status.busy": "2024-06-24T14:48:43.394010Z",
     "iopub.status.idle": "2024-06-24T14:48:44.486782Z",
     "shell.execute_reply": "2024-06-24T14:48:44.485503Z"
    },
    "papermill": {
     "duration": 1.11752,
     "end_time": "2024-06-24T14:48:44.489492",
     "exception": false,
     "start_time": "2024-06-24T14:48:43.371972",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#drop the table\n",
    "df.drop(df[df['KillsWithoutMoving']==True].index,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0324f8be",
   "metadata": {
    "papermill": {
     "duration": 0.020258,
     "end_time": "2024-06-24T14:48:44.530438",
     "exception": false,
     "start_time": "2024-06-24T14:48:44.510180",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**EXTRA ORDINARY ROAD KILLS ??**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77050cf9",
   "metadata": {
    "papermill": {
     "duration": 0.021657,
     "end_time": "2024-06-24T14:48:44.573172",
     "exception": false,
     "start_time": "2024-06-24T14:48:44.551515",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<center><img src  =\"https://media.giphy.com/media/rhaeywi71wkcXRTZK6/giphy.gif\"></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eb39203a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:48:44.617178Z",
     "iopub.status.busy": "2024-06-24T14:48:44.616344Z",
     "iopub.status.idle": "2024-06-24T14:48:44.645226Z",
     "shell.execute_reply": "2024-06-24T14:48:44.644210Z"
    },
    "papermill": {
     "duration": 0.053499,
     "end_time": "2024-06-24T14:48:44.647856",
     "exception": false,
     "start_time": "2024-06-24T14:48:44.594357",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>groupId</th>\n",
       "      <th>matchId</th>\n",
       "      <th>assists</th>\n",
       "      <th>boosts</th>\n",
       "      <th>damageDealt</th>\n",
       "      <th>DBNOs</th>\n",
       "      <th>headshotKills</th>\n",
       "      <th>heals</th>\n",
       "      <th>killPlace</th>\n",
       "      <th>...</th>\n",
       "      <th>swimDistance</th>\n",
       "      <th>teamKills</th>\n",
       "      <th>vehicleDestroys</th>\n",
       "      <th>walkDistance</th>\n",
       "      <th>weaponsAcquired</th>\n",
       "      <th>winPoints</th>\n",
       "      <th>winPlacePerc</th>\n",
       "      <th>PlayersJoined</th>\n",
       "      <th>totalDistance</th>\n",
       "      <th>KillsWithoutMoving</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39338</th>\n",
       "      <td>0097d2ae7a8576</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>72.80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9362</td>\n",
       "      <td>92</td>\n",
       "      <td>5202.80</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90312</th>\n",
       "      <td>0e6ea81fd1b9b3</td>\n",
       "      <td>1e96ac916d6fbb</td>\n",
       "      <td>3f150f3834422c</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>190.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>976.80</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9556</td>\n",
       "      <td>89</td>\n",
       "      <td>4257.80</td>\n",
       "      <td>False</td>\n",
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       "      <th>197752</th>\n",
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       "      <td>2e40495266529f</td>\n",
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       "      <td>2</td>\n",
       "      <td>800.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1516.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6538</td>\n",
       "      <td>89</td>\n",
       "      <td>10085.00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209676</th>\n",
       "      <td>5df28fce2b8128</td>\n",
       "      <td>32a01a5517635a</td>\n",
       "      <td>ee9fb50eb0fe00</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>360.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>59</td>\n",
       "      <td>6142.96</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213544</th>\n",
       "      <td>612ee3024560a3</td>\n",
       "      <td>ab0162b0300c59</td>\n",
       "      <td>7c5535bfd43592</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>426.3</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>128.30</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>96</td>\n",
       "      <td>3607.30</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Id         groupId         matchId  assists  boosts  \\\n",
       "39338   0097d2ae7a8576  b61edfa0c228d4  a786011fe206e9        1       2   \n",
       "90312   0e6ea81fd1b9b3  1e96ac916d6fbb  3f150f3834422c        2       6   \n",
       "197752  6cf1efa197bcc1  28df010b29a9e2  2e40495266529f        0       2   \n",
       "209676  5df28fce2b8128  32a01a5517635a  ee9fb50eb0fe00        0       3   \n",
       "213544  612ee3024560a3  ab0162b0300c59  7c5535bfd43592        1       2   \n",
       "\n",
       "        damageDealt  DBNOs  headshotKills  heals  killPlace  ...  \\\n",
       "39338         495.7      4              0      0          2  ...   \n",
       "90312         190.0      0              0      5          2  ...   \n",
       "197752        800.0      5              0      4          1  ...   \n",
       "209676        360.0      2              0      4          1  ...   \n",
       "213544        426.3      3              0      0          1  ...   \n",
       "\n",
       "        swimDistance  teamKills  vehicleDestroys  walkDistance  \\\n",
       "39338            0.0          0                0         72.80   \n",
       "90312            0.0          0                1        976.80   \n",
       "197752           0.0          0                0       1516.00   \n",
       "209676           0.0          0                0         36.96   \n",
       "213544           0.0          0                0        128.30   \n",
       "\n",
       "        weaponsAcquired winPoints  winPlacePerc  PlayersJoined  totalDistance  \\\n",
       "39338                 1         0        0.9362             92        5202.80   \n",
       "90312                 4         0        0.9556             89        4257.80   \n",
       "197752                3         0        0.6538             89       10085.00   \n",
       "209676                0         0        1.0000             59        6142.96   \n",
       "213544                3         0        0.9167             96        3607.30   \n",
       "\n",
       "        KillsWithoutMoving  \n",
       "39338                False  \n",
       "90312                False  \n",
       "197752               False  \n",
       "209676               False  \n",
       "213544               False  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['roadKills']>5].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4a58a886",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:48:44.691579Z",
     "iopub.status.busy": "2024-06-24T14:48:44.691167Z",
     "iopub.status.idle": "2024-06-24T14:48:44.701538Z",
     "shell.execute_reply": "2024-06-24T14:48:44.700142Z"
    },
    "papermill": {
     "duration": 0.034877,
     "end_time": "2024-06-24T14:48:44.703913",
     "exception": false,
     "start_time": "2024-06-24T14:48:44.669036",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(46, 32)\n"
     ]
    }
   ],
   "source": [
    "print(df[df['roadKills']>5].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cc612a8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:48:44.748342Z",
     "iopub.status.busy": "2024-06-24T14:48:44.747403Z",
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     "shell.execute_reply": "2024-06-24T14:48:46.078303Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Dropping the Extraordinary road kills\n",
    "df.drop(df[df['roadKills']>5].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "93e5f0cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:48:46.126298Z",
     "iopub.status.busy": "2024-06-24T14:48:46.125895Z",
     "iopub.status.idle": "2024-06-24T14:48:46.135977Z",
     "shell.execute_reply": "2024-06-24T14:48:46.134697Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 32)\n"
     ]
    }
   ],
   "source": [
    "# cleared all the extraordinary road kills\n",
    "print(df[df['roadKills']>5].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01526778",
   "metadata": {
    "papermill": {
     "duration": 0.020841,
     "end_time": "2024-06-24T14:48:46.180327",
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     "start_time": "2024-06-24T14:48:46.159486",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## SO Many KILLS -HOW ??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "75c48e26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:48:46.223749Z",
     "iopub.status.busy": "2024-06-24T14:48:46.223349Z",
     "iopub.status.idle": "2024-06-24T14:48:46.251664Z",
     "shell.execute_reply": "2024-06-24T14:48:46.250105Z"
    },
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     "duration": 0.053211,
     "end_time": "2024-06-24T14:48:46.254366",
     "exception": false,
     "start_time": "2024-06-24T14:48:46.201155",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>groupId</th>\n",
       "      <th>matchId</th>\n",
       "      <th>assists</th>\n",
       "      <th>boosts</th>\n",
       "      <th>damageDealt</th>\n",
       "      <th>DBNOs</th>\n",
       "      <th>headshotKills</th>\n",
       "      <th>heals</th>\n",
       "      <th>killPlace</th>\n",
       "      <th>...</th>\n",
       "      <th>swimDistance</th>\n",
       "      <th>teamKills</th>\n",
       "      <th>vehicleDestroys</th>\n",
       "      <th>walkDistance</th>\n",
       "      <th>weaponsAcquired</th>\n",
       "      <th>winPoints</th>\n",
       "      <th>winPlacePerc</th>\n",
       "      <th>PlayersJoined</th>\n",
       "      <th>totalDistance</th>\n",
       "      <th>KillsWithoutMoving</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>97</td>\n",
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       "      <td>0.8000</td>\n",
       "      <td>40</td>\n",
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       "      <td>2</td>\n",
       "      <td>2767.0</td>\n",
       "      <td>24</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3290.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1509</td>\n",
       "      <td>0.9615</td>\n",
       "      <td>97</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40047</th>\n",
       "      <td>8d8d78ba4e9b55</td>\n",
       "      <td>6634da8e3940de</td>\n",
       "      <td>8a728def0644be</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1864.0</td>\n",
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       "      <td>1500</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>54</td>\n",
       "      <td>3532.0</td>\n",
       "      <td>False</td>\n",
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       "      <td>0</td>\n",
       "      <td>1906.0</td>\n",
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       "<p>5 rows × 32 columns</p>\n",
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      "text/plain": [
       "                   Id         groupId         matchId  assists  boosts  \\\n",
       "2569   7711e1d39407da  a9fbc63469296f  45fc89f9b11eaf        1       4   \n",
       "19531  ef706f0f248dbf  f27e63bfc6746c  7d2fd7f5d4f8b2        9       0   \n",
       "37505  44513e16340a63  c1e3f56e09b9a4  cf5dd782577ea9        1       2   \n",
       "40047  8d8d78ba4e9b55  6634da8e3940de  8a728def0644be        3       0   \n",
       "53442  f7a740ffbc2c43  418f6120b2e193  f2614050e9046a        2       0   \n",
       "\n",
       "       damageDealt  DBNOs  headshotKills  heals  killPlace  ...  swimDistance  \\\n",
       "2569        2325.0     13              8      2          1  ...           0.0   \n",
       "19531       2856.0     32              3      0          1  ...           0.0   \n",
       "37505       2767.0     24             17      1          1  ...           0.0   \n",
       "40047       1988.0     22              6      5          5  ...           0.0   \n",
       "53442       1906.0      0              4     19          2  ...           0.0   \n",
       "\n",
       "       teamKills  vehicleDestroys  walkDistance  weaponsAcquired winPoints  \\\n",
       "2569           0                0        2697.0                5      1505   \n",
       "19531          0                0        1136.0               20         0   \n",
       "37505          0                0        3290.0                7      1509   \n",
       "40047          0                0        1864.0               33      1500   \n",
       "53442          0                0         116.7               16         0   \n",
       "\n",
       "       winPlacePerc  PlayersJoined  totalDistance  KillsWithoutMoving  \n",
       "2569         1.0000             97         4709.0               False  \n",
       "19531        0.8000             40         1136.0               False  \n",
       "37505        0.9615             97         5879.0               False  \n",
       "40047        1.0000             54         3532.0               False  \n",
       "53442        1.0000             11          116.7               False  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['kills']>20].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3ed0d238",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x=df['kills']).set_title(\"Distribution of KILLS by a player\")\n",
    "plt.xlabel(\"Number of Kills\")\n",
    "plt.ylabel(\"Count of players\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9f769bc1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:48:49.187810Z",
     "iopub.status.busy": "2024-06-24T14:48:49.187386Z",
     "iopub.status.idle": "2024-06-24T14:48:49.201180Z",
     "shell.execute_reply": "2024-06-24T14:48:49.200026Z"
    },
    "papermill": {
     "duration": 0.039587,
     "end_time": "2024-06-24T14:48:49.203603",
     "exception": false,
     "start_time": "2024-06-24T14:48:49.164016",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1318, 32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['kills']>15].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c4826b66",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:48:49.250758Z",
     "iopub.status.busy": "2024-06-24T14:48:49.249506Z",
     "iopub.status.idle": "2024-06-24T14:48:49.761969Z",
     "shell.execute_reply": "2024-06-24T14:48:49.761000Z"
    },
    "papermill": {
     "duration": 0.538529,
     "end_time": "2024-06-24T14:48:49.764343",
     "exception": false,
     "start_time": "2024-06-24T14:48:49.225814",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Upto 15 kills can ne normal \n",
    "sns.countplot(x=df[df['kills']>=15][\"kills\"]).set_title(\"Distribution of KILLS by a player\")\n",
    "plt.xlabel(\"Number of Kills\")\n",
    "plt.ylabel(\"Count of players\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "021e21e8",
   "metadata": {
    "papermill": {
     "duration": 0.022177,
     "end_time": "2024-06-24T14:48:49.809120",
     "exception": false,
     "start_time": "2024-06-24T14:48:49.786943",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Removing Kills greater than 20 from dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a6378857",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:48:49.856133Z",
     "iopub.status.busy": "2024-06-24T14:48:49.855159Z",
     "iopub.status.idle": "2024-06-24T14:48:49.868369Z",
     "shell.execute_reply": "2024-06-24T14:48:49.867303Z"
    },
    "papermill": {
     "duration": 0.039264,
     "end_time": "2024-06-24T14:48:49.870734",
     "exception": false,
     "start_time": "2024-06-24T14:48:49.831470",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(417, 32)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['kills']>20].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6e859654",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:48:49.918382Z",
     "iopub.status.busy": "2024-06-24T14:48:49.917925Z",
     "iopub.status.idle": "2024-06-24T14:48:51.115642Z",
     "shell.execute_reply": "2024-06-24T14:48:51.114477Z"
    },
    "papermill": {
     "duration": 1.224439,
     "end_time": "2024-06-24T14:48:51.118230",
     "exception": false,
     "start_time": "2024-06-24T14:48:49.893791",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Dropping all 417 vlaues from given dataset\n",
    "df.drop(df[df['kills']>20].index,inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b8e2a689",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:48:51.167599Z",
     "iopub.status.busy": "2024-06-24T14:48:51.167194Z",
     "iopub.status.idle": "2024-06-24T14:48:51.178397Z",
     "shell.execute_reply": "2024-06-24T14:48:51.177314Z"
    },
    "papermill": {
     "duration": 0.037546,
     "end_time": "2024-06-24T14:48:51.180659",
     "exception": false,
     "start_time": "2024-06-24T14:48:51.143113",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['kills']>20].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "826c8e98",
   "metadata": {
    "papermill": {
     "duration": 0.022331,
     "end_time": "2024-06-24T14:48:51.225978",
     "exception": false,
     "start_time": "2024-06-24T14:48:51.203647",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## HeadShot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b505fcc8",
   "metadata": {
    "papermill": {
     "duration": 0.022502,
     "end_time": "2024-06-24T14:48:51.271236",
     "exception": false,
     "start_time": "2024-06-24T14:48:51.248734",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<center><img src  =\"https://media.giphy.com/media/Y0mmEpprOh5R9GZA0J/giphy.gif\"></center>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "493f646f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:48:51.318206Z",
     "iopub.status.busy": "2024-06-24T14:48:51.317811Z",
     "iopub.status.idle": "2024-06-24T14:48:51.367788Z",
     "shell.execute_reply": "2024-06-24T14:48:51.366524Z"
    },
    "papermill": {
     "duration": 0.076665,
     "end_time": "2024-06-24T14:48:51.370567",
     "exception": false,
     "start_time": "2024-06-24T14:48:51.293902",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['Headshot_rate']=df['headshotKills']/df['kills']\n",
    "df['Headshot_rate']=df['Headshot_rate'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49002ec5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:48:51.418083Z",
     "iopub.status.busy": "2024-06-24T14:48:51.417675Z",
     "iopub.status.idle": "2024-06-24T14:49:10.957426Z",
     "shell.execute_reply": "2024-06-24T14:49:10.956300Z"
    },
    "papermill": {
     "duration": 19.566289,
     "end_time": "2024-06-24T14:49:10.959784",
     "exception": false,
     "start_time": "2024-06-24T14:48:51.393495",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Headshot_rate'],bins=10).set_title(\"Histogram showing the distribuyion of headshot rate\")\n",
    "plt.ylabel(\"Count of Players\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "08863c52",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:11.009028Z",
     "iopub.status.busy": "2024-06-24T14:49:11.008647Z",
     "iopub.status.idle": "2024-06-24T14:49:11.030657Z",
     "shell.execute_reply": "2024-06-24T14:49:11.029572Z"
    },
    "papermill": {
     "duration": 0.049697,
     "end_time": "2024-06-24T14:49:11.033356",
     "exception": false,
     "start_time": "2024-06-24T14:49:10.983659",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 33)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Headshot_rate']==1)& (df['kills']>5)].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "753637f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:11.083095Z",
     "iopub.status.busy": "2024-06-24T14:49:11.082706Z",
     "iopub.status.idle": "2024-06-24T14:49:12.466572Z",
     "shell.execute_reply": "2024-06-24T14:49:12.465486Z"
    },
    "papermill": {
     "duration": 1.412479,
     "end_time": "2024-06-24T14:49:12.469320",
     "exception": false,
     "start_time": "2024-06-24T14:49:11.056841",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.drop(df[(df['Headshot_rate']==1)& (df['kills']>6)].index,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "032575da",
   "metadata": {
    "papermill": {
     "duration": 0.031689,
     "end_time": "2024-06-24T14:49:12.525293",
     "exception": false,
     "start_time": "2024-06-24T14:49:12.493604",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Longest Shot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "363f10b5",
   "metadata": {
    "papermill": {
     "duration": 0.029522,
     "end_time": "2024-06-24T14:49:12.584360",
     "exception": false,
     "start_time": "2024-06-24T14:49:12.554838",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<center><img src  =\"https://tenor.com/bluCZ.gif\"></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "49fd1e35",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:12.632880Z",
     "iopub.status.busy": "2024-06-24T14:49:12.632486Z",
     "iopub.status.idle": "2024-06-24T14:49:30.033888Z",
     "shell.execute_reply": "2024-06-24T14:49:30.032781Z"
    },
    "papermill": {
     "duration": 17.428313,
     "end_time": "2024-06-24T14:49:30.036165",
     "exception": false,
     "start_time": "2024-06-24T14:49:12.607852",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['longestKill'],bins=50).set_title(\"Histogram Shoeing Longest Kill Distribution\")\n",
    "plt.ylabel('Count of Players')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7eb8dd8b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:30.086202Z",
     "iopub.status.busy": "2024-06-24T14:49:30.085824Z",
     "iopub.status.idle": "2024-06-24T14:49:30.101560Z",
     "shell.execute_reply": "2024-06-24T14:49:30.100534Z"
    },
    "papermill": {
     "duration": 0.043396,
     "end_time": "2024-06-24T14:49:30.103886",
     "exception": false,
     "start_time": "2024-06-24T14:49:30.060490",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1745, 33)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['longestKill']>500].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7d6a5fa1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:30.154207Z",
     "iopub.status.busy": "2024-06-24T14:49:30.153790Z",
     "iopub.status.idle": "2024-06-24T14:49:31.393529Z",
     "shell.execute_reply": "2024-06-24T14:49:31.392448Z"
    },
    "papermill": {
     "duration": 1.267701,
     "end_time": "2024-06-24T14:49:31.396069",
     "exception": false,
     "start_time": "2024-06-24T14:49:30.128368",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.drop(df[df['longestKill']>500].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d7885ca8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:31.446280Z",
     "iopub.status.busy": "2024-06-24T14:49:31.445878Z",
     "iopub.status.idle": "2024-06-24T14:49:31.458308Z",
     "shell.execute_reply": "2024-06-24T14:49:31.457303Z"
    },
    "papermill": {
     "duration": 0.040406,
     "end_time": "2024-06-24T14:49:31.460537",
     "exception": false,
     "start_time": "2024-06-24T14:49:31.420131",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 33)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['longestKill']>500].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79225d1f",
   "metadata": {
    "papermill": {
     "duration": 0.024007,
     "end_time": "2024-06-24T14:49:31.508676",
     "exception": false,
     "start_time": "2024-06-24T14:49:31.484669",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Weapon Change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bda89e12",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:31.559120Z",
     "iopub.status.busy": "2024-06-24T14:49:31.558046Z",
     "iopub.status.idle": "2024-06-24T14:49:49.169203Z",
     "shell.execute_reply": "2024-06-24T14:49:49.168121Z"
    },
    "papermill": {
     "duration": 17.63859,
     "end_time": "2024-06-24T14:49:49.171425",
     "exception": false,
     "start_time": "2024-06-24T14:49:31.532835",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['weaponsAcquired'],bins=100).set_title(\"Weapon Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "81a7807a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:49.222472Z",
     "iopub.status.busy": "2024-06-24T14:49:49.222031Z",
     "iopub.status.idle": "2024-06-24T14:49:49.240428Z",
     "shell.execute_reply": "2024-06-24T14:49:49.239337Z"
    },
    "papermill": {
     "duration": 0.046715,
     "end_time": "2024-06-24T14:49:49.243108",
     "exception": false,
     "start_time": "2024-06-24T14:49:49.196393",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5435, 33)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping the weapons changed more than 50 times\n",
    "df[df['weaponsAcquired']>15].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ed27a6de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:49.294865Z",
     "iopub.status.busy": "2024-06-24T14:49:49.293733Z",
     "iopub.status.idle": "2024-06-24T14:49:50.545187Z",
     "shell.execute_reply": "2024-06-24T14:49:50.544064Z"
    },
    "papermill": {
     "duration": 1.280033,
     "end_time": "2024-06-24T14:49:50.547873",
     "exception": false,
     "start_time": "2024-06-24T14:49:49.267840",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.drop(df[df['weaponsAcquired']>15].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "580bc796",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:50.598595Z",
     "iopub.status.busy": "2024-06-24T14:49:50.597681Z",
     "iopub.status.idle": "2024-06-24T14:49:50.609340Z",
     "shell.execute_reply": "2024-06-24T14:49:50.608182Z"
    },
    "papermill": {
     "duration": 0.039363,
     "end_time": "2024-06-24T14:49:50.611654",
     "exception": false,
     "start_time": "2024-06-24T14:49:50.572291",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 33)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['weaponsAcquired']>15].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bbf08b6",
   "metadata": {
    "papermill": {
     "duration": 0.024461,
     "end_time": "2024-06-24T14:49:50.660844",
     "exception": false,
     "start_time": "2024-06-24T14:49:50.636383",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## EDA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d2915990",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:50.712618Z",
     "iopub.status.busy": "2024-06-24T14:49:50.711708Z",
     "iopub.status.idle": "2024-06-24T14:49:50.718434Z",
     "shell.execute_reply": "2024-06-24T14:49:50.717292Z"
    },
    "papermill": {
     "duration": 0.034707,
     "end_time": "2024-06-24T14:49:50.720599",
     "exception": false,
     "start_time": "2024-06-24T14:49:50.685892",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4437682, 33)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e628635c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:50.772982Z",
     "iopub.status.busy": "2024-06-24T14:49:50.771962Z",
     "iopub.status.idle": "2024-06-24T14:49:51.710458Z",
     "shell.execute_reply": "2024-06-24T14:49:51.709414Z"
    },
    "papermill": {
     "duration": 0.967051,
     "end_time": "2024-06-24T14:49:51.712745",
     "exception": false,
     "start_time": "2024-06-24T14:49:50.745694",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                    0\n",
       "groupId               0\n",
       "matchId               0\n",
       "assists               0\n",
       "boosts                0\n",
       "damageDealt           0\n",
       "DBNOs                 0\n",
       "headshotKills         0\n",
       "heals                 0\n",
       "killPlace             0\n",
       "killPoints            0\n",
       "kills                 0\n",
       "killStreaks           0\n",
       "longestKill           0\n",
       "matchDuration         0\n",
       "matchType             0\n",
       "maxPlace              0\n",
       "numGroups             0\n",
       "rankPoints            0\n",
       "revives               0\n",
       "rideDistance          0\n",
       "roadKills             0\n",
       "swimDistance          0\n",
       "teamKills             0\n",
       "vehicleDestroys       0\n",
       "walkDistance          0\n",
       "weaponsAcquired       0\n",
       "winPoints             0\n",
       "winPlacePerc          0\n",
       "PlayersJoined         0\n",
       "totalDistance         0\n",
       "KillsWithoutMoving    0\n",
       "Headshot_rate         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "366f8260",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:51.764818Z",
     "iopub.status.busy": "2024-06-24T14:49:51.763817Z",
     "iopub.status.idle": "2024-06-24T14:49:51.776750Z",
     "shell.execute_reply": "2024-06-24T14:49:51.775462Z"
    },
    "papermill": {
     "duration": 0.041369,
     "end_time": "2024-06-24T14:49:51.779179",
     "exception": false,
     "start_time": "2024-06-24T14:49:51.737810",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4437682 entries, 0 to 4446965\n",
      "Data columns (total 33 columns):\n",
      " #   Column              Dtype  \n",
      "---  ------              -----  \n",
      " 0   Id                  object \n",
      " 1   groupId             object \n",
      " 2   matchId             object \n",
      " 3   assists             int64  \n",
      " 4   boosts              int64  \n",
      " 5   damageDealt         float64\n",
      " 6   DBNOs               int64  \n",
      " 7   headshotKills       int64  \n",
      " 8   heals               int64  \n",
      " 9   killPlace           int64  \n",
      " 10  killPoints          int64  \n",
      " 11  kills               int64  \n",
      " 12  killStreaks         int64  \n",
      " 13  longestKill         float64\n",
      " 14  matchDuration       int64  \n",
      " 15  matchType           object \n",
      " 16  maxPlace            int64  \n",
      " 17  numGroups           int64  \n",
      " 18  rankPoints          int64  \n",
      " 19  revives             int64  \n",
      " 20  rideDistance        float64\n",
      " 21  roadKills           int64  \n",
      " 22  swimDistance        float64\n",
      " 23  teamKills           int64  \n",
      " 24  vehicleDestroys     int64  \n",
      " 25  walkDistance        float64\n",
      " 26  weaponsAcquired     int64  \n",
      " 27  winPoints           int64  \n",
      " 28  winPlacePerc        float64\n",
      " 29  PlayersJoined       int64  \n",
      " 30  totalDistance       float64\n",
      " 31  KillsWithoutMoving  bool   \n",
      " 32  Headshot_rate       float64\n",
      "dtypes: bool(1), float64(8), int64(20), object(4)\n",
      "memory usage: 1.1+ GB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3147dbb1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:51.832209Z",
     "iopub.status.busy": "2024-06-24T14:49:51.831312Z",
     "iopub.status.idle": "2024-06-24T14:49:51.836656Z",
     "shell.execute_reply": "2024-06-24T14:49:51.835316Z"
    },
    "papermill": {
     "duration": 0.033788,
     "end_time": "2024-06-24T14:49:51.838749",
     "exception": false,
     "start_time": "2024-06-24T14:49:51.804961",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "###sns.heatmap(df.corr(),annot=True)\n",
    "#pplt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5581e8",
   "metadata": {
    "papermill": {
     "duration": 0.025402,
     "end_time": "2024-06-24T14:49:51.889367",
     "exception": false,
     "start_time": "2024-06-24T14:49:51.863965",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "48a62836",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:51.943445Z",
     "iopub.status.busy": "2024-06-24T14:49:51.942585Z",
     "iopub.status.idle": "2024-06-24T14:49:51.963999Z",
     "shell.execute_reply": "2024-06-24T14:49:51.963107Z"
    },
    "papermill": {
     "duration": 0.05067,
     "end_time": "2024-06-24T14:49:51.966489",
     "exception": false,
     "start_time": "2024-06-24T14:49:51.915819",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "normalising_factor=(100 - df['PlayersJoined']/100)+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "02364a9c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:52.018434Z",
     "iopub.status.busy": "2024-06-24T14:49:52.017542Z",
     "iopub.status.idle": "2024-06-24T14:49:52.026290Z",
     "shell.execute_reply": "2024-06-24T14:49:52.025310Z"
    },
    "papermill": {
     "duration": 0.036992,
     "end_time": "2024-06-24T14:49:52.028526",
     "exception": false,
     "start_time": "2024-06-24T14:49:51.991534",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          100.04\n",
       "1          100.09\n",
       "2          100.02\n",
       "3          100.09\n",
       "4          100.03\n",
       "            ...  \n",
       "4446961    100.06\n",
       "4446962    100.07\n",
       "4446963    100.02\n",
       "4446964    100.06\n",
       "4446965    100.02\n",
       "Name: PlayersJoined, Length: 4437682, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalising_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5966fee0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:52.081339Z",
     "iopub.status.busy": "2024-06-24T14:49:52.080930Z",
     "iopub.status.idle": "2024-06-24T14:49:52.219681Z",
     "shell.execute_reply": "2024-06-24T14:49:52.218724Z"
    },
    "papermill": {
     "duration": 0.168644,
     "end_time": "2024-06-24T14:49:52.222412",
     "exception": false,
     "start_time": "2024-06-24T14:49:52.053768",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['killsNorm']=df['kills'] * normalising_factor\n",
    "df['damageDealtNorm']=df['damageDealt'] * normalising_factor\n",
    "df['maxPlaceNorm']=df['maxPlace']* normalising_factor\n",
    "df['matchDurationNorm']=df['matchDuration']* normalising_factor\n",
    "\n",
    "df['travelDistance']=df['rideDistance'] +df['swimDistance']+df['walkDistance'] \n",
    "df['healsnboost']=df['heals'] + df['boosts']\n",
    "df['assist']=df['assists'] + df['revives']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "28efa0ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:52.274437Z",
     "iopub.status.busy": "2024-06-24T14:49:52.274037Z",
     "iopub.status.idle": "2024-06-24T14:49:52.281287Z",
     "shell.execute_reply": "2024-06-24T14:49:52.280285Z"
    },
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     "duration": 0.035525,
     "end_time": "2024-06-24T14:49:52.283355",
     "exception": false,
     "start_time": "2024-06-24T14:49:52.247830",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'groupId', 'matchId', 'assists', 'boosts', 'damageDealt', 'DBNOs',\n",
       "       'headshotKills', 'heals', 'killPlace', 'killPoints', 'kills',\n",
       "       'killStreaks', 'longestKill', 'matchDuration', 'matchType', 'maxPlace',\n",
       "       'numGroups', 'rankPoints', 'revives', 'rideDistance', 'roadKills',\n",
       "       'swimDistance', 'teamKills', 'vehicleDestroys', 'walkDistance',\n",
       "       'weaponsAcquired', 'winPoints', 'winPlacePerc', 'PlayersJoined',\n",
       "       'totalDistance', 'KillsWithoutMoving', 'Headshot_rate', 'killsNorm',\n",
       "       'damageDealtNorm', 'maxPlaceNorm', 'matchDurationNorm',\n",
       "       'travelDistance', 'healsnboost', 'assist'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c5ab3a8e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:52.335640Z",
     "iopub.status.busy": "2024-06-24T14:49:52.334689Z",
     "iopub.status.idle": "2024-06-24T14:49:52.789057Z",
     "shell.execute_reply": "2024-06-24T14:49:52.787949Z"
    },
    "papermill": {
     "duration": 0.4833,
     "end_time": "2024-06-24T14:49:52.791725",
     "exception": false,
     "start_time": "2024-06-24T14:49:52.308425",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data=df.drop(columns=['Id','groupId','matchId','assists','boosts','walkDistance','swimDistance','rideDistance','heals','revives','kills','damageDealt','matchDuration','maxPlace'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "996662b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:52.846068Z",
     "iopub.status.busy": "2024-06-24T14:49:52.845674Z",
     "iopub.status.idle": "2024-06-24T14:49:52.870021Z",
     "shell.execute_reply": "2024-06-24T14:49:52.869016Z"
    },
    "papermill": {
     "duration": 0.055146,
     "end_time": "2024-06-24T14:49:52.872289",
     "exception": false,
     "start_time": "2024-06-24T14:49:52.817143",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DBNOs</th>\n",
       "      <th>headshotKills</th>\n",
       "      <th>killPlace</th>\n",
       "      <th>killPoints</th>\n",
       "      <th>killStreaks</th>\n",
       "      <th>longestKill</th>\n",
       "      <th>matchType</th>\n",
       "      <th>numGroups</th>\n",
       "      <th>rankPoints</th>\n",
       "      <th>roadKills</th>\n",
       "      <th>...</th>\n",
       "      <th>totalDistance</th>\n",
       "      <th>KillsWithoutMoving</th>\n",
       "      <th>Headshot_rate</th>\n",
       "      <th>killsNorm</th>\n",
       "      <th>damageDealtNorm</th>\n",
       "      <th>maxPlaceNorm</th>\n",
       "      <th>matchDurationNorm</th>\n",
       "      <th>travelDistance</th>\n",
       "      <th>healsnboost</th>\n",
       "      <th>assist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>1241</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>squad-fpp</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>244.8000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2801.12</td>\n",
       "      <td>130652.24</td>\n",
       "      <td>244.8000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>squad-fpp</td>\n",
       "      <td>25</td>\n",
       "      <td>1484</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1445.0445</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9155.2323</td>\n",
       "      <td>2602.34</td>\n",
       "      <td>177859.93</td>\n",
       "      <td>1445.0445</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>duo</td>\n",
       "      <td>47</td>\n",
       "      <td>1491</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>161.8000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6801.3600</td>\n",
       "      <td>5001.00</td>\n",
       "      <td>131826.36</td>\n",
       "      <td>161.8000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>squad-fpp</td>\n",
       "      <td>30</td>\n",
       "      <td>1408</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>202.7000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3292.9610</td>\n",
       "      <td>3102.79</td>\n",
       "      <td>143729.24</td>\n",
       "      <td>202.7000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>58.53</td>\n",
       "      <td>solo-fpp</td>\n",
       "      <td>95</td>\n",
       "      <td>1560</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>49.7500</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.03</td>\n",
       "      <td>10003.0000</td>\n",
       "      <td>9702.91</td>\n",
       "      <td>142442.72</td>\n",
       "      <td>49.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   DBNOs  headshotKills  killPlace  killPoints  killStreaks  longestKill  \\\n",
       "0      0              0         60        1241            0         0.00   \n",
       "1      0              0         57           0            0         0.00   \n",
       "2      0              0         47           0            0         0.00   \n",
       "3      0              0         75           0            0         0.00   \n",
       "4      0              0         45           0            1        58.53   \n",
       "\n",
       "   matchType  numGroups  rankPoints  roadKills  ...  totalDistance  \\\n",
       "0  squad-fpp         26          -1          0  ...       244.8000   \n",
       "1  squad-fpp         25        1484          0  ...      1445.0445   \n",
       "2        duo         47        1491          0  ...       161.8000   \n",
       "3  squad-fpp         30        1408          0  ...       202.7000   \n",
       "4   solo-fpp         95        1560          0  ...        49.7500   \n",
       "\n",
       "   KillsWithoutMoving  Headshot_rate  killsNorm  damageDealtNorm  \\\n",
       "0               False            0.0       0.00           0.0000   \n",
       "1               False            0.0       0.00        9155.2323   \n",
       "2               False            0.0       0.00        6801.3600   \n",
       "3               False            0.0       0.00        3292.9610   \n",
       "4               False            0.0     100.03       10003.0000   \n",
       "\n",
       "   maxPlaceNorm  matchDurationNorm  travelDistance  healsnboost  assist  \n",
       "0       2801.12          130652.24        244.8000            0       0  \n",
       "1       2602.34          177859.93       1445.0445            0       0  \n",
       "2       5001.00          131826.36        161.8000            0       1  \n",
       "3       3102.79          143729.24        202.7000            0       0  \n",
       "4       9702.91          142442.72         49.7500            0       0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4757f0b",
   "metadata": {
    "papermill": {
     "duration": 0.026649,
     "end_time": "2024-06-24T14:49:52.924849",
     "exception": false,
     "start_time": "2024-06-24T14:49:52.898200",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<center><font color= \"green\">ML - Catboost Model</font></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f9ab013",
   "metadata": {
    "papermill": {
     "duration": 0.02608,
     "end_time": "2024-06-24T14:49:52.976585",
     "exception": false,
     "start_time": "2024-06-24T14:49:52.950505",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Handling Categorical Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "926afa4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:53.030341Z",
     "iopub.status.busy": "2024-06-24T14:49:53.029857Z",
     "iopub.status.idle": "2024-06-24T14:49:53.546520Z",
     "shell.execute_reply": "2024-06-24T14:49:53.545417Z"
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   "source": [
    "x=data.drop(['winPlacePerc'],axis=1)\n",
    "y=data['winPlacePerc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3c8390f3",
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   "outputs": [],
   "source": [
    "features=x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4dfb1594",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:53.661122Z",
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   "outputs": [],
   "source": [
    "x=pd.get_dummies(x,columns=['matchType','KillsWithoutMoving'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "efd46a6f",
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       "   DBNOs  headshotKills  killPlace  killPoints  killStreaks  longestKill  \\\n",
       "0      0              0         60        1241            0         0.00   \n",
       "1      0              0         57           0            0         0.00   \n",
       "2      0              0         47           0            0         0.00   \n",
       "3      0              0         75           0            0         0.00   \n",
       "4      0              0         45           0            1        58.53   \n",
       "\n",
       "   numGroups  rankPoints  roadKills  teamKills  ...  matchType_normal-duo-fpp  \\\n",
       "0         26          -1          0          0  ...                     False   \n",
       "1         25        1484          0          0  ...                     False   \n",
       "2         47        1491          0          0  ...                     False   \n",
       "3         30        1408          0          0  ...                     False   \n",
       "4         95        1560          0          0  ...                     False   \n",
       "\n",
       "   matchType_normal-solo  matchType_normal-solo-fpp  matchType_normal-squad  \\\n",
       "0                  False                      False                   False   \n",
       "1                  False                      False                   False   \n",
       "2                  False                      False                   False   \n",
       "3                  False                      False                   False   \n",
       "4                  False                      False                   False   \n",
       "\n",
       "   matchType_normal-squad-fpp  matchType_solo  matchType_solo-fpp  \\\n",
       "0                       False           False               False   \n",
       "1                       False           False               False   \n",
       "2                       False           False               False   \n",
       "3                       False           False               False   \n",
       "4                       False           False                True   \n",
       "\n",
       "   matchType_squad  matchType_squad-fpp  KillsWithoutMoving_False  \n",
       "0            False                 True                      True  \n",
       "1            False                 True                      True  \n",
       "2            False                False                      True  \n",
       "3            False                 True                      True  \n",
       "4            False                False                      True  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d30e7b58",
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    {
     "data": {
      "text/plain": [
       "(4437682, 40)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b866ce26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:49:55.885079Z",
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   "source": [
    "features=x.columns"
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  },
  {
   "cell_type": "markdown",
   "id": "5dd8c772",
   "metadata": {
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     "start_time": "2024-06-24T14:49:55.918104",
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    "tags": []
   },
   "source": [
    "## Scaling The data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "af96b824",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "sc=StandardScaler()\n",
    "sc.fit(x)\n",
    "x=pd.DataFrame(sc.transform(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "95c16e11",
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       "      <td>-0.405077</td>\n",
       "      <td>-0.808865</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0 -0.582712 -0.383999  0.449564  1.172537 -0.765156 -0.459684 -0.732763   \n",
       "1 -0.582712 -0.383999  0.340274 -0.804724 -0.765156 -0.459684 -0.775734   \n",
       "2 -0.582712 -0.383999 -0.024027 -0.804724 -0.765156 -0.459684  0.169630   \n",
       "3 -0.582712 -0.383999  0.996015 -0.804724 -0.765156 -0.459684 -0.560878   \n",
       "4 -0.582712 -0.383999 -0.096888 -0.804724  0.647425  0.727044  2.232242   \n",
       "\n",
       "         7         8         9   ...        30        31        32        33  \\\n",
       "0 -1.212394 -0.049576 -0.142531  ... -0.033643 -0.005516 -0.013898 -0.008425   \n",
       "1  0.803562 -0.049576 -0.142531  ... -0.033643 -0.005516 -0.013898 -0.008425   \n",
       "2  0.813065 -0.049576 -0.142531  ... -0.033643 -0.005516 -0.013898 -0.008425   \n",
       "3  0.700389 -0.049576 -0.142531  ... -0.033643 -0.005516 -0.013898 -0.008425   \n",
       "4  0.906736 -0.049576 -0.142531  ... -0.033643 -0.005516 -0.013898 -0.008425   \n",
       "\n",
       "         34        35        36        37        38   39  \n",
       "0 -0.054801 -0.206548 -0.370783 -0.405077  1.236300  0.0  \n",
       "1 -0.054801 -0.206548 -0.370783 -0.405077  1.236300  0.0  \n",
       "2 -0.054801 -0.206548 -0.370783 -0.405077 -0.808865  0.0  \n",
       "3 -0.054801 -0.206548 -0.370783 -0.405077  1.236300  0.0  \n",
       "4 -0.054801 -0.206548  2.696994 -0.405077 -0.808865  0.0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11b6608a",
   "metadata": {
    "papermill": {
     "duration": 0.026272,
     "end_time": "2024-06-24T14:50:07.997337",
     "exception": false,
     "start_time": "2024-06-24T14:50:07.971065",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Splitting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e375e243",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:50:08.052303Z",
     "iopub.status.busy": "2024-06-24T14:50:08.051060Z",
     "iopub.status.idle": "2024-06-24T14:50:11.143123Z",
     "shell.execute_reply": "2024-06-24T14:50:11.142083Z"
    },
    "papermill": {
     "duration": 3.1221,
     "end_time": "2024-06-24T14:50:11.145660",
     "exception": false,
     "start_time": "2024-06-24T14:50:08.023560",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.3,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2328eb42",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:50:11.201075Z",
     "iopub.status.busy": "2024-06-24T14:50:11.200152Z",
     "iopub.status.idle": "2024-06-24T14:50:11.205823Z",
     "shell.execute_reply": "2024-06-24T14:50:11.204845Z"
    },
    "papermill": {
     "duration": 0.035491,
     "end_time": "2024-06-24T14:50:11.207962",
     "exception": false,
     "start_time": "2024-06-24T14:50:11.172471",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3106377, 40) (3106377,)\n",
      "(1331305, 40) (1331305,)\n"
     ]
    }
   ],
   "source": [
    "print(xtrain.shape, ytrain.shape)\n",
    "print(xtest.shape, ytest.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd822044",
   "metadata": {
    "papermill": {
     "duration": 0.025769,
     "end_time": "2024-06-24T14:50:11.260220",
     "exception": false,
     "start_time": "2024-06-24T14:50:11.234451",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##  CatBOOST MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ae1c53e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:50:11.314643Z",
     "iopub.status.busy": "2024-06-24T14:50:11.314224Z",
     "iopub.status.idle": "2024-06-24T14:50:11.319032Z",
     "shell.execute_reply": "2024-06-24T14:50:11.317976Z"
    },
    "papermill": {
     "duration": 0.034815,
     "end_time": "2024-06-24T14:50:11.321426",
     "exception": false,
     "start_time": "2024-06-24T14:50:11.286611",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import catboost as cb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a55ffd37",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:50:11.377432Z",
     "iopub.status.busy": "2024-06-24T14:50:11.377021Z",
     "iopub.status.idle": "2024-06-24T14:50:11.413965Z",
     "shell.execute_reply": "2024-06-24T14:50:11.412699Z"
    },
    "papermill": {
     "duration": 0.067648,
     "end_time": "2024-06-24T14:50:11.416430",
     "exception": false,
     "start_time": "2024-06-24T14:50:11.348782",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_dataset=cb.Pool(xtrain,ytrain)\n",
    "test_dataset=cb.Pool(xtest,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "852d6dee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:50:11.471547Z",
     "iopub.status.busy": "2024-06-24T14:50:11.470797Z",
     "iopub.status.idle": "2024-06-24T14:50:11.478018Z",
     "shell.execute_reply": "2024-06-24T14:50:11.476891Z"
    },
    "papermill": {
     "duration": 0.036947,
     "end_time": "2024-06-24T14:50:11.480127",
     "exception": false,
     "start_time": "2024-06-24T14:50:11.443180",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model=cb.CatBoostRegressor(loss_function='RMSE')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "200a2add",
   "metadata": {
    "papermill": {
     "duration": 0.026134,
     "end_time": "2024-06-24T14:50:11.532692",
     "exception": false,
     "start_time": "2024-06-24T14:50:11.506558",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## GRID SEARCH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c44dc577",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T14:50:11.587489Z",
     "iopub.status.busy": "2024-06-24T14:50:11.586716Z",
     "iopub.status.idle": "2024-06-24T15:04:43.799960Z",
     "shell.execute_reply": "2024-06-24T15:04:43.798717Z"
    },
    "papermill": {
     "duration": 872.408489,
     "end_time": "2024-06-24T15:04:43.967541",
     "exception": false,
     "start_time": "2024-06-24T14:50:11.559052",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.5483099\ttest: 0.5486894\tbest: 0.5486894 (0)\ttotal: 322ms\tremaining: 31.9s\n",
      "1:\tlearn: 0.5334608\ttest: 0.5338377\tbest: 0.5338377 (1)\ttotal: 535ms\tremaining: 26.2s\n",
      "2:\tlearn: 0.5190872\ttest: 0.5194634\tbest: 0.5194634 (2)\ttotal: 751ms\tremaining: 24.3s\n",
      "3:\tlearn: 0.5051893\ttest: 0.5055628\tbest: 0.5055628 (3)\ttotal: 957ms\tremaining: 23s\n",
      "4:\tlearn: 0.4917086\ttest: 0.4920781\tbest: 0.4920781 (4)\ttotal: 1.17s\tremaining: 22.2s\n",
      "5:\tlearn: 0.4786880\ttest: 0.4790498\tbest: 0.4790498 (5)\ttotal: 1.37s\tremaining: 21.5s\n",
      "6:\tlearn: 0.4660979\ttest: 0.4664545\tbest: 0.4664545 (6)\ttotal: 1.58s\tremaining: 21s\n",
      "7:\tlearn: 0.4539261\ttest: 0.4542794\tbest: 0.4542794 (7)\ttotal: 1.78s\tremaining: 20.5s\n",
      "8:\tlearn: 0.4421267\ttest: 0.4424768\tbest: 0.4424768 (8)\ttotal: 1.99s\tremaining: 20.1s\n",
      "9:\tlearn: 0.4307003\ttest: 0.4310468\tbest: 0.4310468 (9)\ttotal: 2.2s\tremaining: 19.8s\n",
      "10:\tlearn: 0.4196603\ttest: 0.4200031\tbest: 0.4200031 (10)\ttotal: 2.41s\tremaining: 19.5s\n",
      "11:\tlearn: 0.4089773\ttest: 0.4093146\tbest: 0.4093146 (11)\ttotal: 2.62s\tremaining: 19.2s\n",
      "12:\tlearn: 0.3986585\ttest: 0.3989902\tbest: 0.3989902 (12)\ttotal: 2.82s\tremaining: 18.9s\n",
      "13:\tlearn: 0.3886369\ttest: 0.3889650\tbest: 0.3889650 (13)\ttotal: 3.03s\tremaining: 18.6s\n",
      "14:\tlearn: 0.3789776\ttest: 0.3793017\tbest: 0.3793017 (14)\ttotal: 3.24s\tremaining: 18.4s\n",
      "15:\tlearn: 0.3696654\ttest: 0.3699851\tbest: 0.3699851 (15)\ttotal: 3.44s\tremaining: 18.1s\n",
      "16:\tlearn: 0.3606784\ttest: 0.3609939\tbest: 0.3609939 (16)\ttotal: 3.65s\tremaining: 17.8s\n",
      "17:\tlearn: 0.3519397\ttest: 0.3522520\tbest: 0.3522520 (17)\ttotal: 3.86s\tremaining: 17.6s\n",
      "18:\tlearn: 0.3435284\ttest: 0.3438337\tbest: 0.3438337 (18)\ttotal: 4.06s\tremaining: 17.3s\n",
      "19:\tlearn: 0.3353953\ttest: 0.3356974\tbest: 0.3356974 (19)\ttotal: 4.27s\tremaining: 17.1s\n",
      "20:\tlearn: 0.3275299\ttest: 0.3278279\tbest: 0.3278279 (20)\ttotal: 4.48s\tremaining: 16.9s\n",
      "21:\tlearn: 0.3199598\ttest: 0.3202539\tbest: 0.3202539 (21)\ttotal: 4.68s\tremaining: 16.6s\n",
      "22:\tlearn: 0.3126311\ttest: 0.3129211\tbest: 0.3129211 (22)\ttotal: 4.9s\tremaining: 16.4s\n",
      "23:\tlearn: 0.3055860\ttest: 0.3058712\tbest: 0.3058712 (23)\ttotal: 5.09s\tremaining: 16.1s\n",
      "24:\tlearn: 0.2988205\ttest: 0.2991016\tbest: 0.2991016 (24)\ttotal: 5.3s\tremaining: 15.9s\n",
      "25:\tlearn: 0.2922199\ttest: 0.2924977\tbest: 0.2924977 (25)\ttotal: 5.51s\tremaining: 15.7s\n",
      "26:\tlearn: 0.2859179\ttest: 0.2861933\tbest: 0.2861933 (26)\ttotal: 5.71s\tremaining: 15.4s\n",
      "27:\tlearn: 0.2798267\ttest: 0.2800964\tbest: 0.2800964 (27)\ttotal: 5.91s\tremaining: 15.2s\n",
      "28:\tlearn: 0.2739366\ttest: 0.2742032\tbest: 0.2742032 (28)\ttotal: 6.12s\tremaining: 15s\n",
      "29:\tlearn: 0.2682950\ttest: 0.2685556\tbest: 0.2685556 (29)\ttotal: 6.32s\tremaining: 14.7s\n",
      "30:\tlearn: 0.2628041\ttest: 0.2630606\tbest: 0.2630606 (30)\ttotal: 6.53s\tremaining: 14.5s\n",
      "31:\tlearn: 0.2575633\ttest: 0.2578148\tbest: 0.2578148 (31)\ttotal: 6.72s\tremaining: 14.3s\n",
      "32:\tlearn: 0.2524780\ttest: 0.2527256\tbest: 0.2527256 (32)\ttotal: 6.94s\tremaining: 14.1s\n",
      "33:\tlearn: 0.2475925\ttest: 0.2478373\tbest: 0.2478373 (33)\ttotal: 7.14s\tremaining: 13.9s\n",
      "34:\tlearn: 0.2429065\ttest: 0.2431461\tbest: 0.2431461 (34)\ttotal: 7.34s\tremaining: 13.6s\n",
      "35:\tlearn: 0.2383943\ttest: 0.2386292\tbest: 0.2386292 (35)\ttotal: 7.54s\tremaining: 13.4s\n",
      "36:\tlearn: 0.2340347\ttest: 0.2342666\tbest: 0.2342666 (36)\ttotal: 7.75s\tremaining: 13.2s\n",
      "37:\tlearn: 0.2298767\ttest: 0.2301059\tbest: 0.2301059 (37)\ttotal: 7.96s\tremaining: 13s\n",
      "38:\tlearn: 0.2258546\ttest: 0.2260813\tbest: 0.2260813 (38)\ttotal: 8.21s\tremaining: 12.8s\n",
      "39:\tlearn: 0.2219999\ttest: 0.2222236\tbest: 0.2222236 (39)\ttotal: 8.4s\tremaining: 12.6s\n",
      "40:\tlearn: 0.2182939\ttest: 0.2185147\tbest: 0.2185147 (40)\ttotal: 8.6s\tremaining: 12.4s\n",
      "41:\tlearn: 0.2147374\ttest: 0.2149530\tbest: 0.2149530 (41)\ttotal: 8.81s\tremaining: 12.2s\n",
      "42:\tlearn: 0.2113002\ttest: 0.2115127\tbest: 0.2115127 (42)\ttotal: 9.02s\tremaining: 12s\n",
      "43:\tlearn: 0.2080044\ttest: 0.2082147\tbest: 0.2082147 (43)\ttotal: 9.23s\tremaining: 11.7s\n",
      "44:\tlearn: 0.2048473\ttest: 0.2050537\tbest: 0.2050537 (44)\ttotal: 9.43s\tremaining: 11.5s\n",
      "45:\tlearn: 0.2018350\ttest: 0.2020389\tbest: 0.2020389 (45)\ttotal: 9.63s\tremaining: 11.3s\n",
      "46:\tlearn: 0.1989345\ttest: 0.1991333\tbest: 0.1991333 (46)\ttotal: 9.84s\tremaining: 11.1s\n",
      "47:\tlearn: 0.1961652\ttest: 0.1963586\tbest: 0.1963586 (47)\ttotal: 10.1s\tremaining: 10.9s\n",
      "48:\tlearn: 0.1934815\ttest: 0.1936714\tbest: 0.1936714 (48)\ttotal: 10.3s\tremaining: 10.7s\n",
      "49:\tlearn: 0.1908923\ttest: 0.1910792\tbest: 0.1910792 (49)\ttotal: 10.5s\tremaining: 10.5s\n",
      "50:\tlearn: 0.1884320\ttest: 0.1886171\tbest: 0.1886171 (50)\ttotal: 10.7s\tremaining: 10.2s\n",
      "51:\tlearn: 0.1860917\ttest: 0.1862716\tbest: 0.1862716 (51)\ttotal: 10.9s\tremaining: 10s\n",
      "52:\tlearn: 0.1837943\ttest: 0.1839689\tbest: 0.1839689 (52)\ttotal: 11.1s\tremaining: 9.81s\n",
      "53:\tlearn: 0.1816492\ttest: 0.1818188\tbest: 0.1818188 (53)\ttotal: 11.3s\tremaining: 9.61s\n",
      "54:\tlearn: 0.1795597\ttest: 0.1797282\tbest: 0.1797282 (54)\ttotal: 11.5s\tremaining: 9.39s\n",
      "55:\tlearn: 0.1775456\ttest: 0.1777107\tbest: 0.1777107 (55)\ttotal: 11.7s\tremaining: 9.18s\n",
      "56:\tlearn: 0.1756324\ttest: 0.1757952\tbest: 0.1757952 (56)\ttotal: 11.9s\tremaining: 8.96s\n",
      "57:\tlearn: 0.1737567\ttest: 0.1739146\tbest: 0.1739146 (57)\ttotal: 12.1s\tremaining: 8.75s\n",
      "58:\tlearn: 0.1719999\ttest: 0.1721554\tbest: 0.1721554 (58)\ttotal: 12.3s\tremaining: 8.53s\n",
      "59:\tlearn: 0.1703282\ttest: 0.1704802\tbest: 0.1704802 (59)\ttotal: 12.5s\tremaining: 8.31s\n",
      "60:\tlearn: 0.1686872\ttest: 0.1688348\tbest: 0.1688348 (60)\ttotal: 12.7s\tremaining: 8.1s\n",
      "61:\tlearn: 0.1671403\ttest: 0.1672865\tbest: 0.1672865 (61)\ttotal: 12.9s\tremaining: 7.89s\n",
      "62:\tlearn: 0.1656675\ttest: 0.1658101\tbest: 0.1658101 (62)\ttotal: 13.1s\tremaining: 7.67s\n",
      "63:\tlearn: 0.1642670\ttest: 0.1644058\tbest: 0.1644058 (63)\ttotal: 13.3s\tremaining: 7.46s\n",
      "64:\tlearn: 0.1629142\ttest: 0.1630498\tbest: 0.1630498 (64)\ttotal: 13.5s\tremaining: 7.25s\n",
      "65:\tlearn: 0.1616337\ttest: 0.1617658\tbest: 0.1617658 (65)\ttotal: 13.7s\tremaining: 7.04s\n",
      "66:\tlearn: 0.1603870\ttest: 0.1605157\tbest: 0.1605157 (66)\ttotal: 13.9s\tremaining: 6.82s\n",
      "67:\tlearn: 0.1591328\ttest: 0.1592572\tbest: 0.1592572 (67)\ttotal: 14.1s\tremaining: 6.61s\n",
      "68:\tlearn: 0.1580039\ttest: 0.1581257\tbest: 0.1581257 (68)\ttotal: 14.3s\tremaining: 6.4s\n",
      "69:\tlearn: 0.1568721\ttest: 0.1569897\tbest: 0.1569897 (69)\ttotal: 14.5s\tremaining: 6.2s\n",
      "70:\tlearn: 0.1558327\ttest: 0.1559473\tbest: 0.1559473 (70)\ttotal: 14.7s\tremaining: 5.99s\n",
      "71:\tlearn: 0.1548434\ttest: 0.1549549\tbest: 0.1549549 (71)\ttotal: 14.9s\tremaining: 5.78s\n",
      "72:\tlearn: 0.1538822\ttest: 0.1539906\tbest: 0.1539906 (72)\ttotal: 15.1s\tremaining: 5.57s\n",
      "73:\tlearn: 0.1529109\ttest: 0.1530152\tbest: 0.1530152 (73)\ttotal: 15.3s\tremaining: 5.36s\n",
      "74:\tlearn: 0.1519888\ttest: 0.1520896\tbest: 0.1520896 (74)\ttotal: 15.5s\tremaining: 5.16s\n",
      "75:\tlearn: 0.1511561\ttest: 0.1512532\tbest: 0.1512532 (75)\ttotal: 15.7s\tremaining: 4.95s\n",
      "76:\tlearn: 0.1503414\ttest: 0.1504364\tbest: 0.1504364 (76)\ttotal: 15.9s\tremaining: 4.74s\n",
      "77:\tlearn: 0.1495636\ttest: 0.1496564\tbest: 0.1496564 (77)\ttotal: 16.1s\tremaining: 4.53s\n",
      "78:\tlearn: 0.1488293\ttest: 0.1489200\tbest: 0.1489200 (78)\ttotal: 16.3s\tremaining: 4.32s\n",
      "79:\tlearn: 0.1481197\ttest: 0.1482075\tbest: 0.1482075 (79)\ttotal: 16.5s\tremaining: 4.11s\n",
      "80:\tlearn: 0.1473843\ttest: 0.1474685\tbest: 0.1474685 (80)\ttotal: 16.7s\tremaining: 3.91s\n",
      "81:\tlearn: 0.1467216\ttest: 0.1468029\tbest: 0.1468029 (81)\ttotal: 16.9s\tremaining: 3.7s\n",
      "82:\tlearn: 0.1460394\ttest: 0.1461175\tbest: 0.1461175 (82)\ttotal: 17.1s\tremaining: 3.49s\n",
      "83:\tlearn: 0.1454280\ttest: 0.1455030\tbest: 0.1455030 (83)\ttotal: 17.3s\tremaining: 3.29s\n",
      "84:\tlearn: 0.1448523\ttest: 0.1449255\tbest: 0.1449255 (84)\ttotal: 17.5s\tremaining: 3.08s\n",
      "85:\tlearn: 0.1442446\ttest: 0.1443149\tbest: 0.1443149 (85)\ttotal: 17.7s\tremaining: 2.88s\n",
      "86:\tlearn: 0.1437047\ttest: 0.1437741\tbest: 0.1437741 (86)\ttotal: 17.9s\tremaining: 2.67s\n",
      "87:\tlearn: 0.1431951\ttest: 0.1432617\tbest: 0.1432617 (87)\ttotal: 18.1s\tremaining: 2.47s\n",
      "88:\tlearn: 0.1427015\ttest: 0.1427662\tbest: 0.1427662 (88)\ttotal: 18.3s\tremaining: 2.26s\n",
      "89:\tlearn: 0.1422237\ttest: 0.1422857\tbest: 0.1422857 (89)\ttotal: 18.5s\tremaining: 2.06s\n",
      "90:\tlearn: 0.1417645\ttest: 0.1418229\tbest: 0.1418229 (90)\ttotal: 18.7s\tremaining: 1.85s\n",
      "91:\tlearn: 0.1412622\ttest: 0.1413180\tbest: 0.1413180 (91)\ttotal: 18.9s\tremaining: 1.65s\n",
      "92:\tlearn: 0.1407911\ttest: 0.1408440\tbest: 0.1408440 (92)\ttotal: 19.1s\tremaining: 1.44s\n",
      "93:\tlearn: 0.1403840\ttest: 0.1404335\tbest: 0.1404335 (93)\ttotal: 19.3s\tremaining: 1.23s\n",
      "94:\tlearn: 0.1399886\ttest: 0.1400363\tbest: 0.1400363 (94)\ttotal: 19.5s\tremaining: 1.03s\n",
      "95:\tlearn: 0.1396057\ttest: 0.1396504\tbest: 0.1396504 (95)\ttotal: 19.7s\tremaining: 821ms\n",
      "96:\tlearn: 0.1392269\ttest: 0.1392693\tbest: 0.1392693 (96)\ttotal: 19.9s\tremaining: 616ms\n",
      "97:\tlearn: 0.1388751\ttest: 0.1389168\tbest: 0.1389168 (97)\ttotal: 20.1s\tremaining: 410ms\n",
      "98:\tlearn: 0.1384833\ttest: 0.1385231\tbest: 0.1385231 (98)\ttotal: 20.3s\tremaining: 205ms\n",
      "99:\tlearn: 0.1381491\ttest: 0.1381870\tbest: 0.1381870 (99)\ttotal: 20.5s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.1381869946\n",
      "bestIteration = 99\n",
      "\n",
      "0:\tloss: 0.1381870\tbest: 0.1381870 (0)\ttotal: 23.2s\tremaining: 5m 48s\n",
      "0:\tlearn: 0.5127181\ttest: 0.5130872\tbest: 0.5130872 (0)\ttotal: 241ms\tremaining: 23.8s\n",
      "1:\tlearn: 0.4672045\ttest: 0.4675565\tbest: 0.4675565 (1)\ttotal: 462ms\tremaining: 22.6s\n",
      "2:\tlearn: 0.4264927\ttest: 0.4268229\tbest: 0.4268229 (2)\ttotal: 666ms\tremaining: 21.5s\n",
      "3:\tlearn: 0.3903326\ttest: 0.3906468\tbest: 0.3906468 (3)\ttotal: 879ms\tremaining: 21.1s\n",
      "4:\tlearn: 0.3582507\ttest: 0.3585455\tbest: 0.3585455 (4)\ttotal: 1.08s\tremaining: 20.6s\n",
      "5:\tlearn: 0.3296531\ttest: 0.3299374\tbest: 0.3299374 (5)\ttotal: 1.28s\tremaining: 20.1s\n",
      "6:\tlearn: 0.3042709\ttest: 0.3045449\tbest: 0.3045449 (6)\ttotal: 1.5s\tremaining: 19.9s\n",
      "7:\tlearn: 0.2820302\ttest: 0.2822986\tbest: 0.2822986 (7)\ttotal: 1.7s\tremaining: 19.5s\n",
      "8:\tlearn: 0.2625402\ttest: 0.2627996\tbest: 0.2627996 (8)\ttotal: 1.9s\tremaining: 19.2s\n",
      "9:\tlearn: 0.2450372\ttest: 0.2452859\tbest: 0.2452859 (9)\ttotal: 2.11s\tremaining: 19s\n",
      "10:\tlearn: 0.2298686\ttest: 0.2301000\tbest: 0.2301000 (10)\ttotal: 2.31s\tremaining: 18.7s\n",
      "11:\tlearn: 0.2165565\ttest: 0.2167792\tbest: 0.2167792 (11)\ttotal: 2.51s\tremaining: 18.4s\n",
      "12:\tlearn: 0.2050462\ttest: 0.2052605\tbest: 0.2052605 (12)\ttotal: 2.71s\tremaining: 18.1s\n",
      "13:\tlearn: 0.1950066\ttest: 0.1952100\tbest: 0.1952100 (13)\ttotal: 2.92s\tremaining: 17.9s\n",
      "14:\tlearn: 0.1864280\ttest: 0.1866165\tbest: 0.1866165 (14)\ttotal: 3.13s\tremaining: 17.7s\n",
      "15:\tlearn: 0.1791249\ttest: 0.1792975\tbest: 0.1792975 (15)\ttotal: 3.34s\tremaining: 17.5s\n",
      "16:\tlearn: 0.1725307\ttest: 0.1726859\tbest: 0.1726859 (16)\ttotal: 3.63s\tremaining: 17.7s\n",
      "17:\tlearn: 0.1669522\ttest: 0.1671037\tbest: 0.1671037 (17)\ttotal: 3.91s\tremaining: 17.8s\n",
      "18:\tlearn: 0.1622031\ttest: 0.1623450\tbest: 0.1623450 (18)\ttotal: 4.19s\tremaining: 17.9s\n",
      "19:\tlearn: 0.1579077\ttest: 0.1580336\tbest: 0.1580336 (19)\ttotal: 4.46s\tremaining: 17.9s\n",
      "20:\tlearn: 0.1543690\ttest: 0.1544894\tbest: 0.1544894 (20)\ttotal: 4.72s\tremaining: 17.8s\n",
      "21:\tlearn: 0.1513945\ttest: 0.1515046\tbest: 0.1515046 (21)\ttotal: 5s\tremaining: 17.7s\n",
      "22:\tlearn: 0.1487529\ttest: 0.1488564\tbest: 0.1488564 (22)\ttotal: 5.22s\tremaining: 17.5s\n",
      "23:\tlearn: 0.1462242\ttest: 0.1463176\tbest: 0.1463176 (23)\ttotal: 5.42s\tremaining: 17.2s\n",
      "24:\tlearn: 0.1442835\ttest: 0.1443660\tbest: 0.1443660 (24)\ttotal: 5.62s\tremaining: 16.9s\n",
      "25:\tlearn: 0.1425981\ttest: 0.1426715\tbest: 0.1426715 (25)\ttotal: 5.82s\tremaining: 16.6s\n",
      "26:\tlearn: 0.1409073\ttest: 0.1409707\tbest: 0.1409707 (26)\ttotal: 6.01s\tremaining: 16.3s\n",
      "27:\tlearn: 0.1395005\ttest: 0.1395596\tbest: 0.1395596 (27)\ttotal: 6.2s\tremaining: 15.9s\n",
      "28:\tlearn: 0.1383163\ttest: 0.1383689\tbest: 0.1383689 (28)\ttotal: 6.38s\tremaining: 15.6s\n",
      "29:\tlearn: 0.1372030\ttest: 0.1372491\tbest: 0.1372491 (29)\ttotal: 6.6s\tremaining: 15.4s\n",
      "30:\tlearn: 0.1361138\ttest: 0.1361532\tbest: 0.1361532 (30)\ttotal: 6.81s\tremaining: 15.2s\n",
      "31:\tlearn: 0.1352652\ttest: 0.1353006\tbest: 0.1353006 (31)\ttotal: 6.99s\tremaining: 14.9s\n",
      "32:\tlearn: 0.1344242\ttest: 0.1344509\tbest: 0.1344509 (32)\ttotal: 7.19s\tremaining: 14.6s\n",
      "33:\tlearn: 0.1335766\ttest: 0.1335969\tbest: 0.1335969 (33)\ttotal: 7.43s\tremaining: 14.4s\n",
      "34:\tlearn: 0.1328571\ttest: 0.1328760\tbest: 0.1328760 (34)\ttotal: 7.62s\tremaining: 14.2s\n",
      "35:\tlearn: 0.1322636\ttest: 0.1322754\tbest: 0.1322754 (35)\ttotal: 7.81s\tremaining: 13.9s\n",
      "36:\tlearn: 0.1317394\ttest: 0.1317488\tbest: 0.1317488 (36)\ttotal: 7.99s\tremaining: 13.6s\n",
      "37:\tlearn: 0.1312342\ttest: 0.1312384\tbest: 0.1312384 (37)\ttotal: 8.17s\tremaining: 13.3s\n",
      "38:\tlearn: 0.1306413\ttest: 0.1306384\tbest: 0.1306384 (38)\ttotal: 8.37s\tremaining: 13.1s\n",
      "39:\tlearn: 0.1301252\ttest: 0.1301185\tbest: 0.1301185 (39)\ttotal: 8.57s\tremaining: 12.9s\n",
      "40:\tlearn: 0.1296929\ttest: 0.1296833\tbest: 0.1296833 (40)\ttotal: 8.75s\tremaining: 12.6s\n",
      "41:\tlearn: 0.1288031\ttest: 0.1287946\tbest: 0.1287946 (41)\ttotal: 8.95s\tremaining: 12.4s\n",
      "42:\tlearn: 0.1283154\ttest: 0.1283014\tbest: 0.1283014 (42)\ttotal: 9.15s\tremaining: 12.1s\n",
      "43:\tlearn: 0.1279445\ttest: 0.1279273\tbest: 0.1279273 (43)\ttotal: 9.34s\tremaining: 11.9s\n",
      "44:\tlearn: 0.1275414\ttest: 0.1275162\tbest: 0.1275162 (44)\ttotal: 9.53s\tremaining: 11.6s\n",
      "45:\tlearn: 0.1271929\ttest: 0.1271745\tbest: 0.1271745 (45)\ttotal: 9.71s\tremaining: 11.4s\n",
      "46:\tlearn: 0.1264515\ttest: 0.1264329\tbest: 0.1264329 (46)\ttotal: 9.92s\tremaining: 11.2s\n",
      "47:\tlearn: 0.1260388\ttest: 0.1260157\tbest: 0.1260157 (47)\ttotal: 10.1s\tremaining: 11s\n",
      "48:\tlearn: 0.1257214\ttest: 0.1256944\tbest: 0.1256944 (48)\ttotal: 10.3s\tremaining: 10.7s\n",
      "49:\tlearn: 0.1254176\ttest: 0.1253894\tbest: 0.1253894 (49)\ttotal: 10.5s\tremaining: 10.5s\n",
      "50:\tlearn: 0.1251340\ttest: 0.1251033\tbest: 0.1251033 (50)\ttotal: 10.7s\tremaining: 10.3s\n",
      "51:\tlearn: 0.1248949\ttest: 0.1248631\tbest: 0.1248631 (51)\ttotal: 10.9s\tremaining: 10.1s\n",
      "52:\tlearn: 0.1245956\ttest: 0.1245604\tbest: 0.1245604 (52)\ttotal: 11.1s\tremaining: 9.83s\n",
      "53:\tlearn: 0.1243592\ttest: 0.1243317\tbest: 0.1243317 (53)\ttotal: 11.3s\tremaining: 9.6s\n",
      "54:\tlearn: 0.1241284\ttest: 0.1241003\tbest: 0.1241003 (54)\ttotal: 11.5s\tremaining: 9.38s\n",
      "55:\tlearn: 0.1238921\ttest: 0.1238625\tbest: 0.1238625 (55)\ttotal: 11.7s\tremaining: 9.15s\n",
      "56:\tlearn: 0.1236737\ttest: 0.1236412\tbest: 0.1236412 (56)\ttotal: 11.8s\tremaining: 8.93s\n",
      "57:\tlearn: 0.1233337\ttest: 0.1232970\tbest: 0.1232970 (57)\ttotal: 12s\tremaining: 8.71s\n",
      "58:\tlearn: 0.1227974\ttest: 0.1227604\tbest: 0.1227604 (58)\ttotal: 12.2s\tremaining: 8.5s\n",
      "59:\tlearn: 0.1226055\ttest: 0.1225680\tbest: 0.1225680 (59)\ttotal: 12.4s\tremaining: 8.28s\n",
      "60:\tlearn: 0.1224110\ttest: 0.1223748\tbest: 0.1223748 (60)\ttotal: 12.6s\tremaining: 8.06s\n",
      "61:\tlearn: 0.1221721\ttest: 0.1221337\tbest: 0.1221337 (61)\ttotal: 12.8s\tremaining: 7.86s\n",
      "62:\tlearn: 0.1219949\ttest: 0.1219595\tbest: 0.1219595 (62)\ttotal: 13s\tremaining: 7.63s\n",
      "63:\tlearn: 0.1218222\ttest: 0.1217843\tbest: 0.1217843 (63)\ttotal: 13.2s\tremaining: 7.42s\n",
      "64:\tlearn: 0.1216667\ttest: 0.1216256\tbest: 0.1216256 (64)\ttotal: 13.4s\tremaining: 7.21s\n",
      "65:\tlearn: 0.1215201\ttest: 0.1214789\tbest: 0.1214789 (65)\ttotal: 13.6s\tremaining: 7s\n",
      "66:\tlearn: 0.1213815\ttest: 0.1213448\tbest: 0.1213448 (66)\ttotal: 13.8s\tremaining: 6.78s\n",
      "67:\tlearn: 0.1211789\ttest: 0.1211414\tbest: 0.1211414 (67)\ttotal: 14s\tremaining: 6.58s\n",
      "68:\tlearn: 0.1209072\ttest: 0.1208674\tbest: 0.1208674 (68)\ttotal: 14.2s\tremaining: 6.37s\n",
      "69:\tlearn: 0.1207453\ttest: 0.1207029\tbest: 0.1207029 (69)\ttotal: 14.4s\tremaining: 6.15s\n",
      "70:\tlearn: 0.1205958\ttest: 0.1205515\tbest: 0.1205515 (70)\ttotal: 14.6s\tremaining: 5.95s\n",
      "71:\tlearn: 0.1204442\ttest: 0.1203993\tbest: 0.1203993 (71)\ttotal: 14.7s\tremaining: 5.73s\n",
      "72:\tlearn: 0.1202931\ttest: 0.1202473\tbest: 0.1202473 (72)\ttotal: 14.9s\tremaining: 5.52s\n",
      "73:\tlearn: 0.1198612\ttest: 0.1198136\tbest: 0.1198136 (73)\ttotal: 15.1s\tremaining: 5.31s\n",
      "74:\tlearn: 0.1195959\ttest: 0.1195476\tbest: 0.1195476 (74)\ttotal: 15.3s\tremaining: 5.11s\n",
      "75:\tlearn: 0.1194803\ttest: 0.1194310\tbest: 0.1194310 (75)\ttotal: 15.5s\tremaining: 4.89s\n",
      "76:\tlearn: 0.1193582\ttest: 0.1193109\tbest: 0.1193109 (76)\ttotal: 15.7s\tremaining: 4.68s\n",
      "77:\tlearn: 0.1192482\ttest: 0.1192000\tbest: 0.1192000 (77)\ttotal: 15.9s\tremaining: 4.48s\n",
      "78:\tlearn: 0.1191010\ttest: 0.1190529\tbest: 0.1190529 (78)\ttotal: 16.1s\tremaining: 4.27s\n",
      "79:\tlearn: 0.1189353\ttest: 0.1188859\tbest: 0.1188859 (79)\ttotal: 16.3s\tremaining: 4.07s\n",
      "80:\tlearn: 0.1188038\ttest: 0.1187529\tbest: 0.1187529 (80)\ttotal: 16.5s\tremaining: 3.86s\n",
      "81:\tlearn: 0.1186890\ttest: 0.1186445\tbest: 0.1186445 (81)\ttotal: 16.6s\tremaining: 3.65s\n",
      "82:\tlearn: 0.1185831\ttest: 0.1185378\tbest: 0.1185378 (82)\ttotal: 16.8s\tremaining: 3.45s\n",
      "83:\tlearn: 0.1184619\ttest: 0.1184141\tbest: 0.1184141 (83)\ttotal: 17s\tremaining: 3.24s\n",
      "84:\tlearn: 0.1183212\ttest: 0.1182732\tbest: 0.1182732 (84)\ttotal: 17.2s\tremaining: 3.04s\n",
      "85:\tlearn: 0.1182065\ttest: 0.1181591\tbest: 0.1181591 (85)\ttotal: 17.4s\tremaining: 2.84s\n",
      "86:\tlearn: 0.1181132\ttest: 0.1180645\tbest: 0.1180645 (86)\ttotal: 17.6s\tremaining: 2.63s\n",
      "87:\tlearn: 0.1177547\ttest: 0.1177035\tbest: 0.1177035 (87)\ttotal: 17.8s\tremaining: 2.43s\n",
      "88:\tlearn: 0.1173232\ttest: 0.1172731\tbest: 0.1172731 (88)\ttotal: 18s\tremaining: 2.23s\n",
      "89:\tlearn: 0.1171143\ttest: 0.1170635\tbest: 0.1170635 (89)\ttotal: 18.2s\tremaining: 2.02s\n",
      "90:\tlearn: 0.1169462\ttest: 0.1168933\tbest: 0.1168933 (90)\ttotal: 18.4s\tremaining: 1.82s\n",
      "91:\tlearn: 0.1168365\ttest: 0.1167825\tbest: 0.1167825 (91)\ttotal: 18.6s\tremaining: 1.62s\n",
      "92:\tlearn: 0.1167506\ttest: 0.1166965\tbest: 0.1166965 (92)\ttotal: 18.8s\tremaining: 1.42s\n",
      "93:\tlearn: 0.1166596\ttest: 0.1166045\tbest: 0.1166045 (93)\ttotal: 19s\tremaining: 1.21s\n",
      "94:\tlearn: 0.1165556\ttest: 0.1165044\tbest: 0.1165044 (94)\ttotal: 19.2s\tremaining: 1.01s\n",
      "95:\tlearn: 0.1161241\ttest: 0.1160709\tbest: 0.1160709 (95)\ttotal: 19.4s\tremaining: 808ms\n",
      "96:\tlearn: 0.1160321\ttest: 0.1159847\tbest: 0.1159847 (96)\ttotal: 19.6s\tremaining: 605ms\n",
      "97:\tlearn: 0.1159433\ttest: 0.1158953\tbest: 0.1158953 (97)\ttotal: 19.7s\tremaining: 403ms\n",
      "98:\tlearn: 0.1158487\ttest: 0.1157994\tbest: 0.1157994 (98)\ttotal: 20s\tremaining: 202ms\n",
      "99:\tlearn: 0.1157729\ttest: 0.1157240\tbest: 0.1157240 (99)\ttotal: 20.1s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.1157239702\n",
      "bestIteration = 99\n",
      "\n",
      "1:\tloss: 0.1157240\tbest: 0.1157240 (1)\ttotal: 43.6s\tremaining: 5m 5s\n",
      "0:\tlearn: 0.5483099\ttest: 0.5486894\tbest: 0.5486894 (0)\ttotal: 234ms\tremaining: 34.9s\n",
      "1:\tlearn: 0.5334608\ttest: 0.5338377\tbest: 0.5338377 (1)\ttotal: 442ms\tremaining: 32.7s\n",
      "2:\tlearn: 0.5190872\ttest: 0.5194634\tbest: 0.5194634 (2)\ttotal: 660ms\tremaining: 32.3s\n",
      "3:\tlearn: 0.5051893\ttest: 0.5055628\tbest: 0.5055628 (3)\ttotal: 865ms\tremaining: 31.6s\n",
      "4:\tlearn: 0.4917086\ttest: 0.4920781\tbest: 0.4920781 (4)\ttotal: 1.07s\tremaining: 31.1s\n",
      "5:\tlearn: 0.4786880\ttest: 0.4790498\tbest: 0.4790498 (5)\ttotal: 1.28s\tremaining: 30.7s\n",
      "6:\tlearn: 0.4660979\ttest: 0.4664545\tbest: 0.4664545 (6)\ttotal: 1.48s\tremaining: 30.2s\n",
      "7:\tlearn: 0.4539261\ttest: 0.4542794\tbest: 0.4542794 (7)\ttotal: 1.69s\tremaining: 30s\n",
      "8:\tlearn: 0.4421267\ttest: 0.4424768\tbest: 0.4424768 (8)\ttotal: 1.9s\tremaining: 29.7s\n",
      "9:\tlearn: 0.4307003\ttest: 0.4310468\tbest: 0.4310468 (9)\ttotal: 2.11s\tremaining: 29.5s\n",
      "10:\tlearn: 0.4196603\ttest: 0.4200031\tbest: 0.4200031 (10)\ttotal: 2.31s\tremaining: 29.3s\n",
      "11:\tlearn: 0.4089773\ttest: 0.4093146\tbest: 0.4093146 (11)\ttotal: 2.52s\tremaining: 29s\n",
      "12:\tlearn: 0.3986585\ttest: 0.3989902\tbest: 0.3989902 (12)\ttotal: 2.72s\tremaining: 28.6s\n",
      "13:\tlearn: 0.3886369\ttest: 0.3889650\tbest: 0.3889650 (13)\ttotal: 2.93s\tremaining: 28.5s\n",
      "14:\tlearn: 0.3789776\ttest: 0.3793017\tbest: 0.3793017 (14)\ttotal: 3.14s\tremaining: 28.3s\n",
      "15:\tlearn: 0.3696654\ttest: 0.3699851\tbest: 0.3699851 (15)\ttotal: 3.35s\tremaining: 28s\n",
      "16:\tlearn: 0.3606784\ttest: 0.3609939\tbest: 0.3609939 (16)\ttotal: 3.55s\tremaining: 27.8s\n",
      "17:\tlearn: 0.3519397\ttest: 0.3522520\tbest: 0.3522520 (17)\ttotal: 3.77s\tremaining: 27.6s\n",
      "18:\tlearn: 0.3435284\ttest: 0.3438337\tbest: 0.3438337 (18)\ttotal: 3.96s\tremaining: 27.3s\n",
      "19:\tlearn: 0.3353953\ttest: 0.3356974\tbest: 0.3356974 (19)\ttotal: 4.18s\tremaining: 27.2s\n",
      "20:\tlearn: 0.3275299\ttest: 0.3278279\tbest: 0.3278279 (20)\ttotal: 4.4s\tremaining: 27s\n",
      "21:\tlearn: 0.3199598\ttest: 0.3202539\tbest: 0.3202539 (21)\ttotal: 4.6s\tremaining: 26.8s\n",
      "22:\tlearn: 0.3126311\ttest: 0.3129211\tbest: 0.3129211 (22)\ttotal: 4.82s\tremaining: 26.6s\n",
      "23:\tlearn: 0.3055860\ttest: 0.3058712\tbest: 0.3058712 (23)\ttotal: 5.02s\tremaining: 26.3s\n",
      "24:\tlearn: 0.2988205\ttest: 0.2991016\tbest: 0.2991016 (24)\ttotal: 5.22s\tremaining: 26.1s\n",
      "25:\tlearn: 0.2922199\ttest: 0.2924977\tbest: 0.2924977 (25)\ttotal: 5.44s\tremaining: 25.9s\n",
      "26:\tlearn: 0.2859179\ttest: 0.2861933\tbest: 0.2861933 (26)\ttotal: 5.64s\tremaining: 25.7s\n",
      "27:\tlearn: 0.2798267\ttest: 0.2800964\tbest: 0.2800964 (27)\ttotal: 5.84s\tremaining: 25.4s\n",
      "28:\tlearn: 0.2739366\ttest: 0.2742032\tbest: 0.2742032 (28)\ttotal: 6.05s\tremaining: 25.2s\n",
      "29:\tlearn: 0.2682950\ttest: 0.2685556\tbest: 0.2685556 (29)\ttotal: 6.25s\tremaining: 25s\n",
      "30:\tlearn: 0.2628041\ttest: 0.2630606\tbest: 0.2630606 (30)\ttotal: 6.46s\tremaining: 24.8s\n",
      "31:\tlearn: 0.2575633\ttest: 0.2578148\tbest: 0.2578148 (31)\ttotal: 6.66s\tremaining: 24.6s\n",
      "32:\tlearn: 0.2524780\ttest: 0.2527256\tbest: 0.2527256 (32)\ttotal: 6.88s\tremaining: 24.4s\n",
      "33:\tlearn: 0.2475925\ttest: 0.2478373\tbest: 0.2478373 (33)\ttotal: 7.12s\tremaining: 24.3s\n",
      "34:\tlearn: 0.2429065\ttest: 0.2431461\tbest: 0.2431461 (34)\ttotal: 7.32s\tremaining: 24.1s\n",
      "35:\tlearn: 0.2383943\ttest: 0.2386292\tbest: 0.2386292 (35)\ttotal: 7.52s\tremaining: 23.8s\n",
      "36:\tlearn: 0.2340347\ttest: 0.2342666\tbest: 0.2342666 (36)\ttotal: 7.73s\tremaining: 23.6s\n",
      "37:\tlearn: 0.2298767\ttest: 0.2301059\tbest: 0.2301059 (37)\ttotal: 7.95s\tremaining: 23.4s\n",
      "38:\tlearn: 0.2258546\ttest: 0.2260813\tbest: 0.2260813 (38)\ttotal: 8.16s\tremaining: 23.2s\n",
      "39:\tlearn: 0.2219999\ttest: 0.2222236\tbest: 0.2222236 (39)\ttotal: 8.36s\tremaining: 23s\n",
      "40:\tlearn: 0.2182939\ttest: 0.2185147\tbest: 0.2185147 (40)\ttotal: 8.56s\tremaining: 22.8s\n",
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      "46:\tlearn: 0.1989345\ttest: 0.1991333\tbest: 0.1991333 (46)\ttotal: 9.82s\tremaining: 21.5s\n",
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      "49:\tlearn: 0.1908923\ttest: 0.1910792\tbest: 0.1910792 (49)\ttotal: 10.4s\tremaining: 20.9s\n",
      "50:\tlearn: 0.1884320\ttest: 0.1886171\tbest: 0.1886171 (50)\ttotal: 10.7s\tremaining: 20.7s\n",
      "51:\tlearn: 0.1860917\ttest: 0.1862716\tbest: 0.1862716 (51)\ttotal: 10.9s\tremaining: 20.5s\n",
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      "53:\tlearn: 0.1816492\ttest: 0.1818188\tbest: 0.1818188 (53)\ttotal: 11.3s\tremaining: 20.1s\n",
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      "60:\tlearn: 0.1686872\ttest: 0.1688348\tbest: 0.1688348 (60)\ttotal: 12.7s\tremaining: 18.5s\n",
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      "67:\tlearn: 0.1591328\ttest: 0.1592572\tbest: 0.1592572 (67)\ttotal: 14.1s\tremaining: 17s\n",
      "68:\tlearn: 0.1580039\ttest: 0.1581257\tbest: 0.1581257 (68)\ttotal: 14.3s\tremaining: 16.8s\n",
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      "70:\tlearn: 0.1558327\ttest: 0.1559473\tbest: 0.1559473 (70)\ttotal: 14.7s\tremaining: 16.4s\n",
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      "72:\tlearn: 0.1538822\ttest: 0.1539906\tbest: 0.1539906 (72)\ttotal: 15.1s\tremaining: 16s\n",
      "73:\tlearn: 0.1529109\ttest: 0.1530152\tbest: 0.1530152 (73)\ttotal: 15.4s\tremaining: 15.8s\n",
      "74:\tlearn: 0.1519888\ttest: 0.1520896\tbest: 0.1520896 (74)\ttotal: 15.7s\tremaining: 15.7s\n",
      "75:\tlearn: 0.1511561\ttest: 0.1512532\tbest: 0.1512532 (75)\ttotal: 15.9s\tremaining: 15.5s\n",
      "76:\tlearn: 0.1503414\ttest: 0.1504364\tbest: 0.1504364 (76)\ttotal: 16.2s\tremaining: 15.4s\n",
      "77:\tlearn: 0.1495636\ttest: 0.1496564\tbest: 0.1496564 (77)\ttotal: 16.5s\tremaining: 15.2s\n",
      "78:\tlearn: 0.1488293\ttest: 0.1489200\tbest: 0.1489200 (78)\ttotal: 16.7s\tremaining: 15.1s\n",
      "79:\tlearn: 0.1481197\ttest: 0.1482075\tbest: 0.1482075 (79)\ttotal: 17s\tremaining: 14.9s\n",
      "80:\tlearn: 0.1473843\ttest: 0.1474685\tbest: 0.1474685 (80)\ttotal: 17.2s\tremaining: 14.7s\n",
      "81:\tlearn: 0.1467216\ttest: 0.1468029\tbest: 0.1468029 (81)\ttotal: 17.4s\tremaining: 14.4s\n",
      "82:\tlearn: 0.1460394\ttest: 0.1461175\tbest: 0.1461175 (82)\ttotal: 17.6s\tremaining: 14.2s\n",
      "83:\tlearn: 0.1454280\ttest: 0.1455030\tbest: 0.1455030 (83)\ttotal: 17.8s\tremaining: 14s\n",
      "84:\tlearn: 0.1448523\ttest: 0.1449255\tbest: 0.1449255 (84)\ttotal: 18s\tremaining: 13.8s\n",
      "85:\tlearn: 0.1442446\ttest: 0.1443149\tbest: 0.1443149 (85)\ttotal: 18.2s\tremaining: 13.6s\n",
      "86:\tlearn: 0.1437047\ttest: 0.1437741\tbest: 0.1437741 (86)\ttotal: 18.4s\tremaining: 13.3s\n",
      "87:\tlearn: 0.1431951\ttest: 0.1432617\tbest: 0.1432617 (87)\ttotal: 18.6s\tremaining: 13.1s\n",
      "88:\tlearn: 0.1427015\ttest: 0.1427662\tbest: 0.1427662 (88)\ttotal: 18.8s\tremaining: 12.9s\n",
      "89:\tlearn: 0.1422237\ttest: 0.1422857\tbest: 0.1422857 (89)\ttotal: 19s\tremaining: 12.7s\n",
      "90:\tlearn: 0.1417645\ttest: 0.1418229\tbest: 0.1418229 (90)\ttotal: 19.2s\tremaining: 12.4s\n",
      "91:\tlearn: 0.1412622\ttest: 0.1413180\tbest: 0.1413180 (91)\ttotal: 19.4s\tremaining: 12.2s\n",
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      "93:\tlearn: 0.1403840\ttest: 0.1404335\tbest: 0.1404335 (93)\ttotal: 19.8s\tremaining: 11.8s\n",
      "94:\tlearn: 0.1399886\ttest: 0.1400363\tbest: 0.1400363 (94)\ttotal: 20s\tremaining: 11.6s\n",
      "95:\tlearn: 0.1396057\ttest: 0.1396504\tbest: 0.1396504 (95)\ttotal: 20.2s\tremaining: 11.3s\n",
      "96:\tlearn: 0.1392269\ttest: 0.1392693\tbest: 0.1392693 (96)\ttotal: 20.4s\tremaining: 11.1s\n",
      "97:\tlearn: 0.1388751\ttest: 0.1389168\tbest: 0.1389168 (97)\ttotal: 20.6s\tremaining: 10.9s\n",
      "98:\tlearn: 0.1384833\ttest: 0.1385231\tbest: 0.1385231 (98)\ttotal: 20.8s\tremaining: 10.7s\n",
      "99:\tlearn: 0.1381491\ttest: 0.1381870\tbest: 0.1381870 (99)\ttotal: 21s\tremaining: 10.5s\n",
      "100:\tlearn: 0.1377834\ttest: 0.1378194\tbest: 0.1378194 (100)\ttotal: 21.2s\tremaining: 10.3s\n",
      "101:\tlearn: 0.1374535\ttest: 0.1374881\tbest: 0.1374881 (101)\ttotal: 21.3s\tremaining: 10s\n",
      "102:\tlearn: 0.1371516\ttest: 0.1371840\tbest: 0.1371840 (102)\ttotal: 21.5s\tremaining: 9.82s\n",
      "103:\tlearn: 0.1368543\ttest: 0.1368851\tbest: 0.1368851 (103)\ttotal: 21.7s\tremaining: 9.6s\n",
      "104:\tlearn: 0.1365705\ttest: 0.1365987\tbest: 0.1365987 (104)\ttotal: 21.9s\tremaining: 9.39s\n",
      "105:\tlearn: 0.1362893\ttest: 0.1363156\tbest: 0.1363156 (105)\ttotal: 22.1s\tremaining: 9.18s\n",
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      "107:\tlearn: 0.1357775\ttest: 0.1358013\tbest: 0.1358013 (107)\ttotal: 22.5s\tremaining: 8.74s\n",
      "108:\tlearn: 0.1354726\ttest: 0.1354939\tbest: 0.1354939 (108)\ttotal: 22.7s\tremaining: 8.53s\n",
      "109:\tlearn: 0.1352315\ttest: 0.1352503\tbest: 0.1352503 (109)\ttotal: 22.9s\tremaining: 8.32s\n",
      "110:\tlearn: 0.1349953\ttest: 0.1350130\tbest: 0.1350130 (110)\ttotal: 23.1s\tremaining: 8.1s\n",
      "111:\tlearn: 0.1347614\ttest: 0.1347773\tbest: 0.1347773 (111)\ttotal: 23.3s\tremaining: 7.89s\n",
      "112:\tlearn: 0.1344944\ttest: 0.1345076\tbest: 0.1345076 (112)\ttotal: 23.5s\tremaining: 7.68s\n",
      "113:\tlearn: 0.1342832\ttest: 0.1342942\tbest: 0.1342942 (113)\ttotal: 23.6s\tremaining: 7.46s\n",
      "114:\tlearn: 0.1340553\ttest: 0.1340657\tbest: 0.1340657 (114)\ttotal: 23.8s\tremaining: 7.25s\n",
      "115:\tlearn: 0.1338051\ttest: 0.1338138\tbest: 0.1338138 (115)\ttotal: 24s\tremaining: 7.04s\n",
      "116:\tlearn: 0.1335725\ttest: 0.1335781\tbest: 0.1335781 (116)\ttotal: 24.2s\tremaining: 6.83s\n",
      "117:\tlearn: 0.1333825\ttest: 0.1333857\tbest: 0.1333857 (117)\ttotal: 24.4s\tremaining: 6.62s\n",
      "118:\tlearn: 0.1331502\ttest: 0.1331512\tbest: 0.1331512 (118)\ttotal: 24.6s\tremaining: 6.41s\n",
      "119:\tlearn: 0.1329593\ttest: 0.1329591\tbest: 0.1329591 (119)\ttotal: 24.8s\tremaining: 6.2s\n",
      "120:\tlearn: 0.1327706\ttest: 0.1327690\tbest: 0.1327690 (120)\ttotal: 25s\tremaining: 5.99s\n",
      "121:\tlearn: 0.1325759\ttest: 0.1325740\tbest: 0.1325740 (121)\ttotal: 25.2s\tremaining: 5.78s\n",
      "122:\tlearn: 0.1324024\ttest: 0.1323988\tbest: 0.1323988 (122)\ttotal: 25.4s\tremaining: 5.57s\n",
      "123:\tlearn: 0.1321931\ttest: 0.1321878\tbest: 0.1321878 (123)\ttotal: 25.6s\tremaining: 5.36s\n",
      "124:\tlearn: 0.1320218\ttest: 0.1320154\tbest: 0.1320154 (124)\ttotal: 25.8s\tremaining: 5.15s\n",
      "125:\tlearn: 0.1318649\ttest: 0.1318566\tbest: 0.1318566 (125)\ttotal: 25.9s\tremaining: 4.94s\n",
      "126:\tlearn: 0.1317089\ttest: 0.1316997\tbest: 0.1316997 (126)\ttotal: 26.1s\tremaining: 4.73s\n",
      "127:\tlearn: 0.1315648\ttest: 0.1315538\tbest: 0.1315538 (127)\ttotal: 26.3s\tremaining: 4.52s\n",
      "128:\tlearn: 0.1313859\ttest: 0.1313724\tbest: 0.1313724 (128)\ttotal: 26.5s\tremaining: 4.31s\n",
      "129:\tlearn: 0.1310802\ttest: 0.1310668\tbest: 0.1310668 (129)\ttotal: 26.7s\tremaining: 4.11s\n",
      "130:\tlearn: 0.1309329\ttest: 0.1309176\tbest: 0.1309176 (130)\ttotal: 26.9s\tremaining: 3.9s\n",
      "131:\tlearn: 0.1307878\ttest: 0.1307717\tbest: 0.1307717 (131)\ttotal: 27.1s\tremaining: 3.7s\n",
      "132:\tlearn: 0.1306102\ttest: 0.1305923\tbest: 0.1305923 (132)\ttotal: 27.3s\tremaining: 3.49s\n",
      "133:\tlearn: 0.1304437\ttest: 0.1304242\tbest: 0.1304242 (133)\ttotal: 27.5s\tremaining: 3.28s\n",
      "134:\tlearn: 0.1303144\ttest: 0.1302934\tbest: 0.1302934 (134)\ttotal: 27.7s\tremaining: 3.08s\n",
      "135:\tlearn: 0.1300351\ttest: 0.1300143\tbest: 0.1300143 (135)\ttotal: 27.9s\tremaining: 2.87s\n",
      "136:\tlearn: 0.1299027\ttest: 0.1298809\tbest: 0.1298809 (136)\ttotal: 28.1s\tremaining: 2.66s\n",
      "137:\tlearn: 0.1297442\ttest: 0.1297208\tbest: 0.1297208 (137)\ttotal: 28.3s\tremaining: 2.46s\n",
      "138:\tlearn: 0.1296229\ttest: 0.1295983\tbest: 0.1295983 (138)\ttotal: 28.5s\tremaining: 2.25s\n",
      "139:\tlearn: 0.1294892\ttest: 0.1294635\tbest: 0.1294635 (139)\ttotal: 28.6s\tremaining: 2.05s\n",
      "140:\tlearn: 0.1293678\ttest: 0.1293416\tbest: 0.1293416 (140)\ttotal: 28.8s\tremaining: 1.84s\n",
      "141:\tlearn: 0.1292489\ttest: 0.1292211\tbest: 0.1292211 (141)\ttotal: 29s\tremaining: 1.63s\n",
      "142:\tlearn: 0.1290911\ttest: 0.1290612\tbest: 0.1290612 (142)\ttotal: 29.2s\tremaining: 1.43s\n",
      "143:\tlearn: 0.1289673\ttest: 0.1289372\tbest: 0.1289372 (143)\ttotal: 29.4s\tremaining: 1.22s\n",
      "144:\tlearn: 0.1288555\ttest: 0.1288271\tbest: 0.1288271 (144)\ttotal: 29.6s\tremaining: 1.02s\n",
      "145:\tlearn: 0.1287396\ttest: 0.1287099\tbest: 0.1287099 (145)\ttotal: 29.8s\tremaining: 815ms\n",
      "146:\tlearn: 0.1286152\ttest: 0.1285850\tbest: 0.1285850 (146)\ttotal: 30s\tremaining: 612ms\n",
      "147:\tlearn: 0.1284737\ttest: 0.1284418\tbest: 0.1284418 (147)\ttotal: 30.2s\tremaining: 408ms\n",
      "148:\tlearn: 0.1283666\ttest: 0.1283343\tbest: 0.1283343 (148)\ttotal: 30.3s\tremaining: 204ms\n",
      "149:\tlearn: 0.1282259\ttest: 0.1281915\tbest: 0.1281915 (149)\ttotal: 30.5s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.1281914878\n",
      "bestIteration = 149\n",
      "\n",
      "2:\tloss: 0.1281915\tbest: 0.1157240 (1)\ttotal: 1m 14s\tremaining: 5m 22s\n",
      "0:\tlearn: 0.5127181\ttest: 0.5130872\tbest: 0.5130872 (0)\ttotal: 225ms\tremaining: 33.6s\n",
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      "4:\tlearn: 0.3582507\ttest: 0.3585455\tbest: 0.3585455 (4)\ttotal: 1.06s\tremaining: 30.8s\n",
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      "7:\tlearn: 0.2820302\ttest: 0.2822986\tbest: 0.2822986 (7)\ttotal: 1.67s\tremaining: 29.7s\n",
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      "14:\tlearn: 0.1864280\ttest: 0.1866165\tbest: 0.1866165 (14)\ttotal: 3.1s\tremaining: 27.9s\n",
      "15:\tlearn: 0.1791249\ttest: 0.1792975\tbest: 0.1792975 (15)\ttotal: 3.31s\tremaining: 27.7s\n",
      "16:\tlearn: 0.1725307\ttest: 0.1726859\tbest: 0.1726859 (16)\ttotal: 3.51s\tremaining: 27.5s\n",
      "17:\tlearn: 0.1669522\ttest: 0.1671037\tbest: 0.1671037 (17)\ttotal: 3.71s\tremaining: 27.2s\n",
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      "23:\tlearn: 0.1462242\ttest: 0.1463176\tbest: 0.1463176 (23)\ttotal: 4.9s\tremaining: 25.7s\n",
      "24:\tlearn: 0.1442835\ttest: 0.1443660\tbest: 0.1443660 (24)\ttotal: 5.1s\tremaining: 25.5s\n",
      "25:\tlearn: 0.1425981\ttest: 0.1426715\tbest: 0.1426715 (25)\ttotal: 5.29s\tremaining: 25.2s\n",
      "26:\tlearn: 0.1409073\ttest: 0.1409707\tbest: 0.1409707 (26)\ttotal: 5.49s\tremaining: 25s\n",
      "27:\tlearn: 0.1395005\ttest: 0.1395596\tbest: 0.1395596 (27)\ttotal: 5.68s\tremaining: 24.7s\n",
      "28:\tlearn: 0.1383163\ttest: 0.1383689\tbest: 0.1383689 (28)\ttotal: 5.86s\tremaining: 24.5s\n",
      "29:\tlearn: 0.1372030\ttest: 0.1372491\tbest: 0.1372491 (29)\ttotal: 6.09s\tremaining: 24.3s\n",
      "30:\tlearn: 0.1361138\ttest: 0.1361532\tbest: 0.1361532 (30)\ttotal: 6.32s\tremaining: 24.3s\n",
      "31:\tlearn: 0.1352652\ttest: 0.1353006\tbest: 0.1353006 (31)\ttotal: 6.5s\tremaining: 24s\n",
      "32:\tlearn: 0.1344242\ttest: 0.1344509\tbest: 0.1344509 (32)\ttotal: 6.69s\tremaining: 23.7s\n",
      "33:\tlearn: 0.1335766\ttest: 0.1335969\tbest: 0.1335969 (33)\ttotal: 6.89s\tremaining: 23.5s\n",
      "34:\tlearn: 0.1328571\ttest: 0.1328760\tbest: 0.1328760 (34)\ttotal: 7.08s\tremaining: 23.3s\n",
      "35:\tlearn: 0.1322636\ttest: 0.1322754\tbest: 0.1322754 (35)\ttotal: 7.26s\tremaining: 23s\n",
      "36:\tlearn: 0.1317394\ttest: 0.1317488\tbest: 0.1317488 (36)\ttotal: 7.45s\tremaining: 22.7s\n",
      "37:\tlearn: 0.1312342\ttest: 0.1312384\tbest: 0.1312384 (37)\ttotal: 7.62s\tremaining: 22.5s\n",
      "38:\tlearn: 0.1306413\ttest: 0.1306384\tbest: 0.1306384 (38)\ttotal: 7.82s\tremaining: 22.3s\n",
      "39:\tlearn: 0.1301252\ttest: 0.1301185\tbest: 0.1301185 (39)\ttotal: 8.03s\tremaining: 22.1s\n",
      "40:\tlearn: 0.1296929\ttest: 0.1296833\tbest: 0.1296833 (40)\ttotal: 8.21s\tremaining: 21.8s\n",
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      "43:\tlearn: 0.1279445\ttest: 0.1279273\tbest: 0.1279273 (43)\ttotal: 8.79s\tremaining: 21.2s\n",
      "44:\tlearn: 0.1275414\ttest: 0.1275162\tbest: 0.1275162 (44)\ttotal: 8.98s\tremaining: 21s\n",
      "45:\tlearn: 0.1271929\ttest: 0.1271745\tbest: 0.1271745 (45)\ttotal: 9.16s\tremaining: 20.7s\n",
      "46:\tlearn: 0.1264515\ttest: 0.1264329\tbest: 0.1264329 (46)\ttotal: 9.36s\tremaining: 20.5s\n",
      "47:\tlearn: 0.1260388\ttest: 0.1260157\tbest: 0.1260157 (47)\ttotal: 9.57s\tremaining: 20.3s\n",
      "48:\tlearn: 0.1257214\ttest: 0.1256944\tbest: 0.1256944 (48)\ttotal: 9.77s\tremaining: 20.1s\n",
      "49:\tlearn: 0.1254176\ttest: 0.1253894\tbest: 0.1253894 (49)\ttotal: 9.95s\tremaining: 19.9s\n",
      "50:\tlearn: 0.1251340\ttest: 0.1251033\tbest: 0.1251033 (50)\ttotal: 10.2s\tremaining: 19.7s\n",
      "51:\tlearn: 0.1248949\ttest: 0.1248631\tbest: 0.1248631 (51)\ttotal: 10.3s\tremaining: 19.5s\n",
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      "53:\tlearn: 0.1243592\ttest: 0.1243317\tbest: 0.1243317 (53)\ttotal: 10.7s\tremaining: 19s\n",
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      "59:\tlearn: 0.1226055\ttest: 0.1225680\tbest: 0.1225680 (59)\ttotal: 11.8s\tremaining: 17.8s\n",
      "60:\tlearn: 0.1224110\ttest: 0.1223748\tbest: 0.1223748 (60)\ttotal: 12s\tremaining: 17.5s\n",
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      "63:\tlearn: 0.1218222\ttest: 0.1217843\tbest: 0.1217843 (63)\ttotal: 12.6s\tremaining: 16.9s\n",
      "64:\tlearn: 0.1216667\ttest: 0.1216256\tbest: 0.1216256 (64)\ttotal: 12.8s\tremaining: 16.7s\n",
      "65:\tlearn: 0.1215201\ttest: 0.1214789\tbest: 0.1214789 (65)\ttotal: 13s\tremaining: 16.5s\n",
      "66:\tlearn: 0.1213815\ttest: 0.1213448\tbest: 0.1213448 (66)\ttotal: 13.2s\tremaining: 16.3s\n",
      "67:\tlearn: 0.1211789\ttest: 0.1211414\tbest: 0.1211414 (67)\ttotal: 13.4s\tremaining: 16.1s\n",
      "68:\tlearn: 0.1209072\ttest: 0.1208674\tbest: 0.1208674 (68)\ttotal: 13.6s\tremaining: 15.9s\n",
      "69:\tlearn: 0.1207453\ttest: 0.1207029\tbest: 0.1207029 (69)\ttotal: 13.8s\tremaining: 15.7s\n",
      "70:\tlearn: 0.1205958\ttest: 0.1205515\tbest: 0.1205515 (70)\ttotal: 14s\tremaining: 15.5s\n",
      "71:\tlearn: 0.1204442\ttest: 0.1203993\tbest: 0.1203993 (71)\ttotal: 14.1s\tremaining: 15.3s\n",
      "72:\tlearn: 0.1202931\ttest: 0.1202473\tbest: 0.1202473 (72)\ttotal: 14.3s\tremaining: 15.1s\n",
      "73:\tlearn: 0.1198612\ttest: 0.1198136\tbest: 0.1198136 (73)\ttotal: 14.5s\tremaining: 14.9s\n",
      "74:\tlearn: 0.1195959\ttest: 0.1195476\tbest: 0.1195476 (74)\ttotal: 14.7s\tremaining: 14.7s\n",
      "75:\tlearn: 0.1194803\ttest: 0.1194310\tbest: 0.1194310 (75)\ttotal: 14.9s\tremaining: 14.5s\n",
      "76:\tlearn: 0.1193582\ttest: 0.1193109\tbest: 0.1193109 (76)\ttotal: 15.1s\tremaining: 14.3s\n",
      "77:\tlearn: 0.1192482\ttest: 0.1192000\tbest: 0.1192000 (77)\ttotal: 15.3s\tremaining: 14.1s\n",
      "78:\tlearn: 0.1191010\ttest: 0.1190529\tbest: 0.1190529 (78)\ttotal: 15.5s\tremaining: 13.9s\n",
      "79:\tlearn: 0.1189353\ttest: 0.1188859\tbest: 0.1188859 (79)\ttotal: 15.7s\tremaining: 13.7s\n",
      "80:\tlearn: 0.1188038\ttest: 0.1187529\tbest: 0.1187529 (80)\ttotal: 15.9s\tremaining: 13.5s\n",
      "81:\tlearn: 0.1186890\ttest: 0.1186445\tbest: 0.1186445 (81)\ttotal: 16.1s\tremaining: 13.3s\n",
      "82:\tlearn: 0.1185831\ttest: 0.1185378\tbest: 0.1185378 (82)\ttotal: 16.3s\tremaining: 13.2s\n",
      "83:\tlearn: 0.1184619\ttest: 0.1184141\tbest: 0.1184141 (83)\ttotal: 16.5s\tremaining: 13s\n",
      "84:\tlearn: 0.1183212\ttest: 0.1182732\tbest: 0.1182732 (84)\ttotal: 16.8s\tremaining: 12.8s\n",
      "85:\tlearn: 0.1182065\ttest: 0.1181591\tbest: 0.1181591 (85)\ttotal: 17.1s\tremaining: 12.7s\n",
      "86:\tlearn: 0.1181132\ttest: 0.1180645\tbest: 0.1180645 (86)\ttotal: 17.3s\tremaining: 12.5s\n",
      "87:\tlearn: 0.1177547\ttest: 0.1177035\tbest: 0.1177035 (87)\ttotal: 17.6s\tremaining: 12.4s\n",
      "88:\tlearn: 0.1173232\ttest: 0.1172731\tbest: 0.1172731 (88)\ttotal: 17.8s\tremaining: 12.2s\n",
      "89:\tlearn: 0.1171143\ttest: 0.1170635\tbest: 0.1170635 (89)\ttotal: 18.1s\tremaining: 12.1s\n",
      "90:\tlearn: 0.1169462\ttest: 0.1168933\tbest: 0.1168933 (90)\ttotal: 18.3s\tremaining: 11.9s\n",
      "91:\tlearn: 0.1168365\ttest: 0.1167825\tbest: 0.1167825 (91)\ttotal: 18.5s\tremaining: 11.7s\n",
      "92:\tlearn: 0.1167506\ttest: 0.1166965\tbest: 0.1166965 (92)\ttotal: 18.7s\tremaining: 11.5s\n",
      "93:\tlearn: 0.1166596\ttest: 0.1166045\tbest: 0.1166045 (93)\ttotal: 18.9s\tremaining: 11.3s\n",
      "94:\tlearn: 0.1165556\ttest: 0.1165044\tbest: 0.1165044 (94)\ttotal: 19.1s\tremaining: 11.1s\n",
      "95:\tlearn: 0.1161241\ttest: 0.1160709\tbest: 0.1160709 (95)\ttotal: 19.3s\tremaining: 10.9s\n",
      "96:\tlearn: 0.1160321\ttest: 0.1159847\tbest: 0.1159847 (96)\ttotal: 19.5s\tremaining: 10.6s\n",
      "97:\tlearn: 0.1159433\ttest: 0.1158953\tbest: 0.1158953 (97)\ttotal: 19.7s\tremaining: 10.4s\n",
      "98:\tlearn: 0.1158487\ttest: 0.1157994\tbest: 0.1157994 (98)\ttotal: 19.9s\tremaining: 10.2s\n",
      "99:\tlearn: 0.1157729\ttest: 0.1157240\tbest: 0.1157240 (99)\ttotal: 20.1s\tremaining: 10s\n",
      "100:\tlearn: 0.1157008\ttest: 0.1156516\tbest: 0.1156516 (100)\ttotal: 20.2s\tremaining: 9.82s\n",
      "101:\tlearn: 0.1155802\ttest: 0.1155303\tbest: 0.1155303 (101)\ttotal: 20.4s\tremaining: 9.62s\n",
      "102:\tlearn: 0.1154709\ttest: 0.1154189\tbest: 0.1154189 (102)\ttotal: 20.6s\tremaining: 9.41s\n",
      "103:\tlearn: 0.1154043\ttest: 0.1153526\tbest: 0.1153526 (103)\ttotal: 20.8s\tremaining: 9.21s\n",
      "104:\tlearn: 0.1150454\ttest: 0.1149908\tbest: 0.1149908 (104)\ttotal: 21s\tremaining: 9.01s\n",
      "105:\tlearn: 0.1149626\ttest: 0.1149104\tbest: 0.1149104 (105)\ttotal: 21.2s\tremaining: 8.8s\n",
      "106:\tlearn: 0.1148965\ttest: 0.1148449\tbest: 0.1148449 (106)\ttotal: 21.4s\tremaining: 8.59s\n",
      "107:\tlearn: 0.1145676\ttest: 0.1145174\tbest: 0.1145174 (107)\ttotal: 21.6s\tremaining: 8.4s\n",
      "108:\tlearn: 0.1144807\ttest: 0.1144312\tbest: 0.1144312 (108)\ttotal: 21.8s\tremaining: 8.19s\n",
      "109:\tlearn: 0.1144050\ttest: 0.1143570\tbest: 0.1143570 (109)\ttotal: 21.9s\tremaining: 7.98s\n",
      "110:\tlearn: 0.1143030\ttest: 0.1142544\tbest: 0.1142544 (110)\ttotal: 22.2s\tremaining: 7.79s\n",
      "111:\tlearn: 0.1141755\ttest: 0.1141228\tbest: 0.1141228 (111)\ttotal: 22.3s\tremaining: 7.58s\n",
      "112:\tlearn: 0.1141009\ttest: 0.1140487\tbest: 0.1140487 (112)\ttotal: 22.5s\tremaining: 7.38s\n",
      "113:\tlearn: 0.1140368\ttest: 0.1139839\tbest: 0.1139839 (113)\ttotal: 22.7s\tremaining: 7.17s\n",
      "114:\tlearn: 0.1139767\ttest: 0.1139230\tbest: 0.1139230 (114)\ttotal: 22.9s\tremaining: 6.97s\n",
      "115:\tlearn: 0.1139212\ttest: 0.1138673\tbest: 0.1138673 (115)\ttotal: 23.1s\tremaining: 6.76s\n",
      "116:\tlearn: 0.1138679\ttest: 0.1138137\tbest: 0.1138137 (116)\ttotal: 23.3s\tremaining: 6.56s\n",
      "117:\tlearn: 0.1135813\ttest: 0.1135231\tbest: 0.1135231 (117)\ttotal: 23.5s\tremaining: 6.37s\n",
      "118:\tlearn: 0.1135048\ttest: 0.1134508\tbest: 0.1134508 (118)\ttotal: 23.7s\tremaining: 6.16s\n",
      "119:\tlearn: 0.1134414\ttest: 0.1133876\tbest: 0.1133876 (119)\ttotal: 23.8s\tremaining: 5.96s\n",
      "120:\tlearn: 0.1133747\ttest: 0.1133240\tbest: 0.1133240 (120)\ttotal: 24s\tremaining: 5.75s\n",
      "121:\tlearn: 0.1133136\ttest: 0.1132628\tbest: 0.1132628 (121)\ttotal: 24.2s\tremaining: 5.55s\n",
      "122:\tlearn: 0.1130864\ttest: 0.1130332\tbest: 0.1130332 (122)\ttotal: 24.4s\tremaining: 5.35s\n",
      "123:\tlearn: 0.1130288\ttest: 0.1129751\tbest: 0.1129751 (123)\ttotal: 24.6s\tremaining: 5.15s\n",
      "124:\tlearn: 0.1128328\ttest: 0.1127794\tbest: 0.1127794 (124)\ttotal: 24.8s\tremaining: 4.95s\n",
      "125:\tlearn: 0.1127692\ttest: 0.1127152\tbest: 0.1127152 (125)\ttotal: 25s\tremaining: 4.76s\n",
      "126:\tlearn: 0.1125139\ttest: 0.1124631\tbest: 0.1124631 (126)\ttotal: 25.2s\tremaining: 4.56s\n",
      "127:\tlearn: 0.1124494\ttest: 0.1124032\tbest: 0.1124032 (127)\ttotal: 25.4s\tremaining: 4.36s\n",
      "128:\tlearn: 0.1122360\ttest: 0.1121884\tbest: 0.1121884 (128)\ttotal: 25.6s\tremaining: 4.16s\n",
      "129:\tlearn: 0.1121449\ttest: 0.1120971\tbest: 0.1120971 (129)\ttotal: 25.7s\tremaining: 3.96s\n",
      "130:\tlearn: 0.1119960\ttest: 0.1119467\tbest: 0.1119467 (130)\ttotal: 25.9s\tremaining: 3.76s\n",
      "131:\tlearn: 0.1119455\ttest: 0.1118960\tbest: 0.1118960 (131)\ttotal: 26.1s\tremaining: 3.56s\n",
      "132:\tlearn: 0.1118910\ttest: 0.1118408\tbest: 0.1118408 (132)\ttotal: 26.4s\tremaining: 3.37s\n",
      "133:\tlearn: 0.1117820\ttest: 0.1117284\tbest: 0.1117284 (133)\ttotal: 26.5s\tremaining: 3.17s\n",
      "134:\tlearn: 0.1117227\ttest: 0.1116690\tbest: 0.1116690 (134)\ttotal: 26.7s\tremaining: 2.97s\n",
      "135:\tlearn: 0.1116761\ttest: 0.1116223\tbest: 0.1116223 (135)\ttotal: 26.9s\tremaining: 2.77s\n",
      "136:\tlearn: 0.1114741\ttest: 0.1114181\tbest: 0.1114181 (136)\ttotal: 27.1s\tremaining: 2.57s\n",
      "137:\tlearn: 0.1112349\ttest: 0.1111756\tbest: 0.1111756 (137)\ttotal: 27.3s\tremaining: 2.38s\n",
      "138:\tlearn: 0.1109757\ttest: 0.1109156\tbest: 0.1109156 (138)\ttotal: 27.5s\tremaining: 2.18s\n",
      "139:\tlearn: 0.1109127\ttest: 0.1108582\tbest: 0.1108582 (139)\ttotal: 27.7s\tremaining: 1.98s\n",
      "140:\tlearn: 0.1108035\ttest: 0.1107468\tbest: 0.1107468 (140)\ttotal: 27.9s\tremaining: 1.78s\n",
      "141:\tlearn: 0.1106905\ttest: 0.1106348\tbest: 0.1106348 (141)\ttotal: 28.1s\tremaining: 1.58s\n",
      "142:\tlearn: 0.1106197\ttest: 0.1105633\tbest: 0.1105633 (142)\ttotal: 28.3s\tremaining: 1.38s\n",
      "143:\tlearn: 0.1105541\ttest: 0.1104964\tbest: 0.1104964 (143)\ttotal: 28.5s\tremaining: 1.19s\n",
      "144:\tlearn: 0.1104187\ttest: 0.1103622\tbest: 0.1103622 (144)\ttotal: 28.7s\tremaining: 988ms\n",
      "145:\tlearn: 0.1103547\ttest: 0.1103025\tbest: 0.1103025 (145)\ttotal: 28.8s\tremaining: 790ms\n",
      "146:\tlearn: 0.1102709\ttest: 0.1102171\tbest: 0.1102171 (146)\ttotal: 29s\tremaining: 593ms\n",
      "147:\tlearn: 0.1102283\ttest: 0.1101736\tbest: 0.1101736 (147)\ttotal: 29.2s\tremaining: 395ms\n",
      "148:\tlearn: 0.1099992\ttest: 0.1099395\tbest: 0.1099395 (148)\ttotal: 29.4s\tremaining: 197ms\n",
      "149:\tlearn: 0.1099427\ttest: 0.1098878\tbest: 0.1098878 (149)\ttotal: 29.6s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.1098878199\n",
      "bestIteration = 149\n",
      "\n",
      "3:\tloss: 0.1098878\tbest: 0.1098878 (3)\ttotal: 1m 44s\tremaining: 5m 12s\n",
      "0:\tlearn: 0.5479383\ttest: 0.5483108\tbest: 0.5483108 (0)\ttotal: 304ms\tremaining: 30.1s\n",
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      "4:\tlearn: 0.4900053\ttest: 0.4903470\tbest: 0.4903470 (4)\ttotal: 1.41s\tremaining: 26.8s\n",
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      "11:\tlearn: 0.4050653\ttest: 0.4053647\tbest: 0.4053647 (11)\ttotal: 3.36s\tremaining: 24.7s\n",
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      "14:\tlearn: 0.3742468\ttest: 0.3745265\tbest: 0.3745265 (14)\ttotal: 4.21s\tremaining: 23.8s\n",
      "15:\tlearn: 0.3646277\ttest: 0.3649016\tbest: 0.3649016 (15)\ttotal: 4.5s\tremaining: 23.6s\n",
      "16:\tlearn: 0.3553219\ttest: 0.3555889\tbest: 0.3555889 (16)\ttotal: 4.77s\tremaining: 23.3s\n",
      "17:\tlearn: 0.3463447\ttest: 0.3466063\tbest: 0.3466063 (17)\ttotal: 5.04s\tremaining: 23s\n",
      "18:\tlearn: 0.3377139\ttest: 0.3379723\tbest: 0.3379723 (18)\ttotal: 5.33s\tremaining: 22.7s\n",
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      "20:\tlearn: 0.3211741\ttest: 0.3214212\tbest: 0.3214212 (20)\ttotal: 5.88s\tremaining: 22.1s\n",
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      "22:\tlearn: 0.3057991\ttest: 0.3060369\tbest: 0.3060369 (22)\ttotal: 6.49s\tremaining: 21.7s\n",
      "23:\tlearn: 0.2984885\ttest: 0.2987212\tbest: 0.2987212 (23)\ttotal: 6.8s\tremaining: 21.5s\n",
      "24:\tlearn: 0.2914361\ttest: 0.2916624\tbest: 0.2916624 (24)\ttotal: 7.08s\tremaining: 21.2s\n",
      "25:\tlearn: 0.2846297\ttest: 0.2848525\tbest: 0.2848525 (25)\ttotal: 7.37s\tremaining: 21s\n",
      "26:\tlearn: 0.2780623\ttest: 0.2782831\tbest: 0.2782831 (26)\ttotal: 7.63s\tremaining: 20.6s\n",
      "27:\tlearn: 0.2717128\ttest: 0.2719290\tbest: 0.2719290 (27)\ttotal: 7.92s\tremaining: 20.4s\n",
      "28:\tlearn: 0.2655919\ttest: 0.2658037\tbest: 0.2658037 (28)\ttotal: 8.21s\tremaining: 20.1s\n",
      "29:\tlearn: 0.2597185\ttest: 0.2599268\tbest: 0.2599268 (29)\ttotal: 8.48s\tremaining: 19.8s\n",
      "30:\tlearn: 0.2540615\ttest: 0.2542664\tbest: 0.2542664 (30)\ttotal: 8.75s\tremaining: 19.5s\n",
      "31:\tlearn: 0.2485315\ttest: 0.2487339\tbest: 0.2487339 (31)\ttotal: 9.03s\tremaining: 19.2s\n",
      "32:\tlearn: 0.2432815\ttest: 0.2434852\tbest: 0.2434852 (32)\ttotal: 9.31s\tremaining: 18.9s\n",
      "33:\tlearn: 0.2381752\ttest: 0.2383743\tbest: 0.2383743 (33)\ttotal: 9.59s\tremaining: 18.6s\n",
      "34:\tlearn: 0.2332250\ttest: 0.2334169\tbest: 0.2334169 (34)\ttotal: 9.87s\tremaining: 18.3s\n",
      "35:\tlearn: 0.2284476\ttest: 0.2286341\tbest: 0.2286341 (35)\ttotal: 10.2s\tremaining: 18.1s\n",
      "36:\tlearn: 0.2238389\ttest: 0.2240179\tbest: 0.2240179 (36)\ttotal: 10.4s\tremaining: 17.8s\n",
      "37:\tlearn: 0.2194540\ttest: 0.2196303\tbest: 0.2196303 (37)\ttotal: 10.7s\tremaining: 17.5s\n",
      "38:\tlearn: 0.2152537\ttest: 0.2154281\tbest: 0.2154281 (38)\ttotal: 11s\tremaining: 17.2s\n",
      "39:\tlearn: 0.2111402\ttest: 0.2113086\tbest: 0.2113086 (39)\ttotal: 11.3s\tremaining: 16.9s\n",
      "40:\tlearn: 0.2071880\ttest: 0.2073491\tbest: 0.2073491 (40)\ttotal: 11.6s\tremaining: 16.6s\n",
      "41:\tlearn: 0.2034034\ttest: 0.2035631\tbest: 0.2035631 (41)\ttotal: 11.8s\tremaining: 16.3s\n",
      "42:\tlearn: 0.1996319\ttest: 0.1997904\tbest: 0.1997904 (42)\ttotal: 12.1s\tremaining: 16.1s\n",
      "43:\tlearn: 0.1961421\ttest: 0.1962983\tbest: 0.1962983 (43)\ttotal: 12.4s\tremaining: 15.8s\n",
      "44:\tlearn: 0.1927747\ttest: 0.1929290\tbest: 0.1929290 (44)\ttotal: 12.7s\tremaining: 15.5s\n",
      "45:\tlearn: 0.1894201\ttest: 0.1895721\tbest: 0.1895721 (45)\ttotal: 13s\tremaining: 15.2s\n",
      "46:\tlearn: 0.1863413\ttest: 0.1864913\tbest: 0.1864913 (46)\ttotal: 13.2s\tremaining: 14.9s\n",
      "47:\tlearn: 0.1833548\ttest: 0.1835005\tbest: 0.1835005 (47)\ttotal: 13.5s\tremaining: 14.6s\n",
      "48:\tlearn: 0.1804557\ttest: 0.1805950\tbest: 0.1805950 (48)\ttotal: 13.8s\tremaining: 14.4s\n",
      "49:\tlearn: 0.1776735\ttest: 0.1778071\tbest: 0.1778071 (49)\ttotal: 14.1s\tremaining: 14.1s\n",
      "50:\tlearn: 0.1750152\ttest: 0.1751489\tbest: 0.1751489 (50)\ttotal: 14.4s\tremaining: 13.8s\n",
      "51:\tlearn: 0.1724860\ttest: 0.1726160\tbest: 0.1726160 (51)\ttotal: 14.7s\tremaining: 13.5s\n",
      "52:\tlearn: 0.1700367\ttest: 0.1701604\tbest: 0.1701604 (52)\ttotal: 15s\tremaining: 13.3s\n",
      "53:\tlearn: 0.1675732\ttest: 0.1676939\tbest: 0.1676939 (53)\ttotal: 15.2s\tremaining: 13s\n",
      "54:\tlearn: 0.1653115\ttest: 0.1654281\tbest: 0.1654281 (54)\ttotal: 15.5s\tremaining: 12.7s\n",
      "55:\tlearn: 0.1630557\ttest: 0.1631705\tbest: 0.1631705 (55)\ttotal: 15.8s\tremaining: 12.4s\n",
      "56:\tlearn: 0.1609823\ttest: 0.1610915\tbest: 0.1610915 (56)\ttotal: 16.1s\tremaining: 12.2s\n",
      "57:\tlearn: 0.1589668\ttest: 0.1590709\tbest: 0.1590709 (57)\ttotal: 16.5s\tremaining: 11.9s\n",
      "58:\tlearn: 0.1570551\ttest: 0.1571536\tbest: 0.1571536 (58)\ttotal: 16.7s\tremaining: 11.6s\n",
      "59:\tlearn: 0.1552328\ttest: 0.1553257\tbest: 0.1553257 (59)\ttotal: 17s\tremaining: 11.3s\n",
      "60:\tlearn: 0.1533662\ttest: 0.1534570\tbest: 0.1534570 (60)\ttotal: 17.3s\tremaining: 11.1s\n",
      "61:\tlearn: 0.1516498\ttest: 0.1517361\tbest: 0.1517361 (61)\ttotal: 17.6s\tremaining: 10.8s\n",
      "62:\tlearn: 0.1500526\ttest: 0.1501342\tbest: 0.1501342 (62)\ttotal: 17.9s\tremaining: 10.5s\n",
      "63:\tlearn: 0.1484182\ttest: 0.1484967\tbest: 0.1484967 (63)\ttotal: 18.2s\tremaining: 10.2s\n",
      "64:\tlearn: 0.1469270\ttest: 0.1470026\tbest: 0.1470026 (64)\ttotal: 18.4s\tremaining: 9.93s\n",
      "65:\tlearn: 0.1455293\ttest: 0.1456003\tbest: 0.1456003 (65)\ttotal: 18.7s\tremaining: 9.64s\n",
      "66:\tlearn: 0.1442002\ttest: 0.1442666\tbest: 0.1442666 (66)\ttotal: 19.1s\tremaining: 9.39s\n",
      "67:\tlearn: 0.1429005\ttest: 0.1429619\tbest: 0.1429619 (67)\ttotal: 19.5s\tremaining: 9.17s\n",
      "68:\tlearn: 0.1416355\ttest: 0.1416926\tbest: 0.1416926 (68)\ttotal: 19.9s\tremaining: 8.92s\n",
      "69:\tlearn: 0.1404363\ttest: 0.1404892\tbest: 0.1404892 (69)\ttotal: 20.2s\tremaining: 8.67s\n",
      "70:\tlearn: 0.1392832\ttest: 0.1393309\tbest: 0.1393309 (70)\ttotal: 20.6s\tremaining: 8.41s\n",
      "71:\tlearn: 0.1381982\ttest: 0.1382418\tbest: 0.1382418 (71)\ttotal: 20.9s\tremaining: 8.12s\n",
      "72:\tlearn: 0.1371771\ttest: 0.1372180\tbest: 0.1372180 (72)\ttotal: 21.1s\tremaining: 7.82s\n",
      "73:\tlearn: 0.1361892\ttest: 0.1362268\tbest: 0.1362268 (73)\ttotal: 21.4s\tremaining: 7.53s\n",
      "74:\tlearn: 0.1352440\ttest: 0.1352772\tbest: 0.1352772 (74)\ttotal: 21.7s\tremaining: 7.24s\n",
      "75:\tlearn: 0.1343486\ttest: 0.1343778\tbest: 0.1343778 (75)\ttotal: 22s\tremaining: 6.94s\n",
      "76:\tlearn: 0.1334724\ttest: 0.1334991\tbest: 0.1334991 (76)\ttotal: 22.3s\tremaining: 6.65s\n",
      "77:\tlearn: 0.1326487\ttest: 0.1326726\tbest: 0.1326726 (77)\ttotal: 22.5s\tremaining: 6.35s\n",
      "78:\tlearn: 0.1316902\ttest: 0.1317115\tbest: 0.1317115 (78)\ttotal: 22.8s\tremaining: 6.07s\n",
      "79:\tlearn: 0.1308884\ttest: 0.1309085\tbest: 0.1309085 (79)\ttotal: 23.1s\tremaining: 5.77s\n",
      "80:\tlearn: 0.1301325\ttest: 0.1301513\tbest: 0.1301513 (80)\ttotal: 23.4s\tremaining: 5.48s\n",
      "81:\tlearn: 0.1294368\ttest: 0.1294520\tbest: 0.1294520 (81)\ttotal: 23.6s\tremaining: 5.19s\n",
      "82:\tlearn: 0.1286166\ttest: 0.1286299\tbest: 0.1286299 (82)\ttotal: 23.9s\tremaining: 4.9s\n",
      "83:\tlearn: 0.1279498\ttest: 0.1279595\tbest: 0.1279595 (83)\ttotal: 24.2s\tremaining: 4.61s\n",
      "84:\tlearn: 0.1272749\ttest: 0.1272807\tbest: 0.1272807 (84)\ttotal: 24.5s\tremaining: 4.32s\n",
      "85:\tlearn: 0.1266679\ttest: 0.1266733\tbest: 0.1266733 (85)\ttotal: 24.7s\tremaining: 4.03s\n",
      "86:\tlearn: 0.1260717\ttest: 0.1260762\tbest: 0.1260762 (86)\ttotal: 25s\tremaining: 3.73s\n",
      "87:\tlearn: 0.1255138\ttest: 0.1255160\tbest: 0.1255160 (87)\ttotal: 25.3s\tremaining: 3.45s\n",
      "88:\tlearn: 0.1249902\ttest: 0.1249919\tbest: 0.1249919 (88)\ttotal: 25.5s\tremaining: 3.16s\n",
      "89:\tlearn: 0.1244859\ttest: 0.1244847\tbest: 0.1244847 (89)\ttotal: 25.8s\tremaining: 2.87s\n",
      "90:\tlearn: 0.1240077\ttest: 0.1240066\tbest: 0.1240066 (90)\ttotal: 26s\tremaining: 2.57s\n",
      "91:\tlearn: 0.1235292\ttest: 0.1235265\tbest: 0.1235265 (91)\ttotal: 26.3s\tremaining: 2.29s\n",
      "92:\tlearn: 0.1230898\ttest: 0.1230853\tbest: 0.1230853 (92)\ttotal: 26.6s\tremaining: 2s\n",
      "93:\tlearn: 0.1226829\ttest: 0.1226761\tbest: 0.1226761 (93)\ttotal: 26.9s\tremaining: 1.72s\n",
      "94:\tlearn: 0.1222340\ttest: 0.1222244\tbest: 0.1222244 (94)\ttotal: 27.2s\tremaining: 1.43s\n",
      "95:\tlearn: 0.1217686\ttest: 0.1217575\tbest: 0.1217575 (95)\ttotal: 27.5s\tremaining: 1.14s\n",
      "96:\tlearn: 0.1213581\ttest: 0.1213435\tbest: 0.1213435 (96)\ttotal: 27.8s\tremaining: 859ms\n",
      "97:\tlearn: 0.1209920\ttest: 0.1209759\tbest: 0.1209759 (97)\ttotal: 28s\tremaining: 572ms\n",
      "98:\tlearn: 0.1206217\ttest: 0.1206032\tbest: 0.1206032 (98)\ttotal: 28.3s\tremaining: 286ms\n",
      "99:\tlearn: 0.1201525\ttest: 0.1201343\tbest: 0.1201343 (99)\ttotal: 28.6s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.1201343155\n",
      "bestIteration = 99\n",
      "\n",
      "4:\tloss: 0.1201343\tbest: 0.1098878 (3)\ttotal: 2m 12s\tremaining: 4m 52s\n",
      "0:\tlearn: 0.5114392\ttest: 0.5117850\tbest: 0.5117850 (0)\ttotal: 301ms\tremaining: 29.8s\n",
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      "2:\tlearn: 0.4230285\ttest: 0.4233284\tbest: 0.4233284 (2)\ttotal: 856ms\tremaining: 27.7s\n",
      "3:\tlearn: 0.3859195\ttest: 0.3862066\tbest: 0.3862066 (3)\ttotal: 1.12s\tremaining: 26.9s\n",
      "4:\tlearn: 0.3528177\ttest: 0.3530876\tbest: 0.3530876 (4)\ttotal: 1.39s\tremaining: 26.5s\n",
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      "6:\tlearn: 0.2973212\ttest: 0.2975520\tbest: 0.2975520 (6)\ttotal: 1.95s\tremaining: 25.9s\n",
      "7:\tlearn: 0.2742760\ttest: 0.2744923\tbest: 0.2744923 (7)\ttotal: 2.23s\tremaining: 25.7s\n",
      "8:\tlearn: 0.2538644\ttest: 0.2540683\tbest: 0.2540683 (8)\ttotal: 2.5s\tremaining: 25.3s\n",
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      "14:\tlearn: 0.1740155\ttest: 0.1741211\tbest: 0.1741211 (14)\ttotal: 4.2s\tremaining: 23.8s\n",
      "15:\tlearn: 0.1659856\ttest: 0.1660719\tbest: 0.1660719 (15)\ttotal: 4.49s\tremaining: 23.6s\n",
      "16:\tlearn: 0.1590518\ttest: 0.1591195\tbest: 0.1591195 (16)\ttotal: 4.77s\tremaining: 23.3s\n",
      "17:\tlearn: 0.1532044\ttest: 0.1532553\tbest: 0.1532553 (17)\ttotal: 5.07s\tremaining: 23.1s\n",
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      "23:\tlearn: 0.1309475\ttest: 0.1309418\tbest: 0.1309418 (23)\ttotal: 6.72s\tremaining: 21.3s\n",
      "24:\tlearn: 0.1288058\ttest: 0.1287958\tbest: 0.1287958 (24)\ttotal: 7.01s\tremaining: 21s\n",
      "25:\tlearn: 0.1264772\ttest: 0.1264589\tbest: 0.1264589 (25)\ttotal: 7.3s\tremaining: 20.8s\n",
      "26:\tlearn: 0.1248442\ttest: 0.1248177\tbest: 0.1248177 (26)\ttotal: 7.55s\tremaining: 20.4s\n",
      "27:\tlearn: 0.1233962\ttest: 0.1233607\tbest: 0.1233607 (27)\ttotal: 7.88s\tremaining: 20.3s\n",
      "28:\tlearn: 0.1217939\ttest: 0.1217505\tbest: 0.1217505 (28)\ttotal: 8.17s\tremaining: 20s\n",
      "29:\tlearn: 0.1203213\ttest: 0.1202784\tbest: 0.1202784 (29)\ttotal: 8.44s\tremaining: 19.7s\n",
      "30:\tlearn: 0.1193770\ttest: 0.1193300\tbest: 0.1193300 (30)\ttotal: 8.67s\tremaining: 19.3s\n",
      "31:\tlearn: 0.1185694\ttest: 0.1185169\tbest: 0.1185169 (31)\ttotal: 8.92s\tremaining: 19s\n",
      "32:\tlearn: 0.1178179\ttest: 0.1177626\tbest: 0.1177626 (32)\ttotal: 9.17s\tremaining: 18.6s\n",
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      "35:\tlearn: 0.1151104\ttest: 0.1150628\tbest: 0.1150628 (35)\ttotal: 9.98s\tremaining: 17.7s\n",
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      "37:\tlearn: 0.1140529\ttest: 0.1139975\tbest: 0.1139975 (37)\ttotal: 10.5s\tremaining: 17.1s\n",
      "38:\tlearn: 0.1136177\ttest: 0.1135685\tbest: 0.1135685 (38)\ttotal: 10.7s\tremaining: 16.8s\n",
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      "45:\tlearn: 0.1101271\ttest: 0.1100724\tbest: 0.1100724 (45)\ttotal: 12.6s\tremaining: 14.8s\n",
      "46:\tlearn: 0.1098495\ttest: 0.1097927\tbest: 0.1097927 (46)\ttotal: 12.8s\tremaining: 14.5s\n",
      "47:\tlearn: 0.1095134\ttest: 0.1094577\tbest: 0.1094577 (47)\ttotal: 13.1s\tremaining: 14.2s\n",
      "48:\tlearn: 0.1092284\ttest: 0.1091731\tbest: 0.1091731 (48)\ttotal: 13.3s\tremaining: 13.8s\n",
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      "54:\tlearn: 0.1063119\ttest: 0.1062650\tbest: 0.1062650 (54)\ttotal: 14.9s\tremaining: 12.2s\n",
      "55:\tlearn: 0.1057307\ttest: 0.1056830\tbest: 0.1056830 (55)\ttotal: 15.2s\tremaining: 12s\n",
      "56:\tlearn: 0.1055557\ttest: 0.1055056\tbest: 0.1055056 (56)\ttotal: 15.5s\tremaining: 11.7s\n",
      "57:\tlearn: 0.1049559\ttest: 0.1049002\tbest: 0.1049002 (57)\ttotal: 15.8s\tremaining: 11.4s\n",
      "58:\tlearn: 0.1047867\ttest: 0.1047307\tbest: 0.1047307 (58)\ttotal: 16s\tremaining: 11.1s\n",
      "59:\tlearn: 0.1046258\ttest: 0.1045715\tbest: 0.1045715 (59)\ttotal: 16.2s\tremaining: 10.8s\n",
      "60:\tlearn: 0.1044777\ttest: 0.1044291\tbest: 0.1044291 (60)\ttotal: 16.5s\tremaining: 10.5s\n",
      "61:\tlearn: 0.1042200\ttest: 0.1041685\tbest: 0.1041685 (61)\ttotal: 16.7s\tremaining: 10.2s\n",
      "62:\tlearn: 0.1037437\ttest: 0.1036886\tbest: 0.1036886 (62)\ttotal: 17s\tremaining: 9.97s\n",
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      "64:\tlearn: 0.1031740\ttest: 0.1031168\tbest: 0.1031168 (64)\ttotal: 17.5s\tremaining: 9.44s\n",
      "65:\tlearn: 0.1026624\ttest: 0.1026100\tbest: 0.1026100 (65)\ttotal: 17.9s\tremaining: 9.22s\n",
      "66:\tlearn: 0.1024032\ttest: 0.1023573\tbest: 0.1023573 (66)\ttotal: 18.2s\tremaining: 8.94s\n",
      "67:\tlearn: 0.1022857\ttest: 0.1022408\tbest: 0.1022408 (67)\ttotal: 18.4s\tremaining: 8.65s\n",
      "68:\tlearn: 0.1021304\ttest: 0.1020850\tbest: 0.1020850 (68)\ttotal: 18.6s\tremaining: 8.36s\n",
      "69:\tlearn: 0.1020107\ttest: 0.1019658\tbest: 0.1019658 (69)\ttotal: 18.9s\tremaining: 8.08s\n",
      "70:\tlearn: 0.1015506\ttest: 0.1015000\tbest: 0.1015000 (70)\ttotal: 19.2s\tremaining: 7.83s\n",
      "71:\tlearn: 0.1014088\ttest: 0.1013582\tbest: 0.1013582 (71)\ttotal: 19.4s\tremaining: 7.56s\n",
      "72:\tlearn: 0.1012238\ttest: 0.1011709\tbest: 0.1011709 (72)\ttotal: 19.7s\tremaining: 7.28s\n",
      "73:\tlearn: 0.1008002\ttest: 0.1007649\tbest: 0.1007649 (73)\ttotal: 20s\tremaining: 7.01s\n",
      "74:\tlearn: 0.1006813\ttest: 0.1006452\tbest: 0.1006452 (74)\ttotal: 20.2s\tremaining: 6.73s\n",
      "75:\tlearn: 0.1003852\ttest: 0.1003492\tbest: 0.1003492 (75)\ttotal: 20.4s\tremaining: 6.46s\n",
      "76:\tlearn: 0.1002606\ttest: 0.1002250\tbest: 0.1002250 (76)\ttotal: 20.7s\tremaining: 6.18s\n",
      "77:\tlearn: 0.0998953\ttest: 0.0998704\tbest: 0.0998704 (77)\ttotal: 21s\tremaining: 5.93s\n",
      "78:\tlearn: 0.0997665\ttest: 0.0997415\tbest: 0.0997415 (78)\ttotal: 21.3s\tremaining: 5.65s\n",
      "79:\tlearn: 0.0996514\ttest: 0.0996273\tbest: 0.0996273 (79)\ttotal: 21.5s\tremaining: 5.38s\n",
      "80:\tlearn: 0.0993499\ttest: 0.0993321\tbest: 0.0993321 (80)\ttotal: 21.8s\tremaining: 5.12s\n",
      "81:\tlearn: 0.0991752\ttest: 0.0991613\tbest: 0.0991613 (81)\ttotal: 22.1s\tremaining: 4.84s\n",
      "82:\tlearn: 0.0989762\ttest: 0.0989562\tbest: 0.0989562 (82)\ttotal: 22.4s\tremaining: 4.59s\n",
      "83:\tlearn: 0.0988576\ttest: 0.0988365\tbest: 0.0988365 (83)\ttotal: 22.8s\tremaining: 4.34s\n",
      "84:\tlearn: 0.0985836\ttest: 0.0985605\tbest: 0.0985605 (84)\ttotal: 23.2s\tremaining: 4.09s\n",
      "85:\tlearn: 0.0984605\ttest: 0.0984413\tbest: 0.0984413 (85)\ttotal: 23.5s\tremaining: 3.83s\n",
      "86:\tlearn: 0.0983802\ttest: 0.0983608\tbest: 0.0983608 (86)\ttotal: 23.8s\tremaining: 3.56s\n",
      "87:\tlearn: 0.0982989\ttest: 0.0982835\tbest: 0.0982835 (87)\ttotal: 24.1s\tremaining: 3.28s\n",
      "88:\tlearn: 0.0982072\ttest: 0.0981930\tbest: 0.0981930 (88)\ttotal: 24.3s\tremaining: 3.01s\n",
      "89:\tlearn: 0.0980241\ttest: 0.0980089\tbest: 0.0980089 (89)\ttotal: 24.6s\tremaining: 2.73s\n",
      "90:\tlearn: 0.0979189\ttest: 0.0979052\tbest: 0.0979052 (90)\ttotal: 24.8s\tremaining: 2.46s\n",
      "91:\tlearn: 0.0977694\ttest: 0.0977527\tbest: 0.0977527 (91)\ttotal: 25.1s\tremaining: 2.18s\n",
      "92:\tlearn: 0.0976050\ttest: 0.0975895\tbest: 0.0975895 (92)\ttotal: 25.3s\tremaining: 1.9s\n",
      "93:\tlearn: 0.0974042\ttest: 0.0973934\tbest: 0.0973934 (93)\ttotal: 25.6s\tremaining: 1.63s\n",
      "94:\tlearn: 0.0973102\ttest: 0.0973008\tbest: 0.0973008 (94)\ttotal: 25.8s\tremaining: 1.36s\n",
      "95:\tlearn: 0.0971026\ttest: 0.0970903\tbest: 0.0970903 (95)\ttotal: 26.1s\tremaining: 1.09s\n",
      "96:\tlearn: 0.0970026\ttest: 0.0969893\tbest: 0.0969893 (96)\ttotal: 26.4s\tremaining: 815ms\n",
      "97:\tlearn: 0.0969279\ttest: 0.0969142\tbest: 0.0969142 (97)\ttotal: 26.6s\tremaining: 543ms\n",
      "98:\tlearn: 0.0968616\ttest: 0.0968484\tbest: 0.0968484 (98)\ttotal: 26.8s\tremaining: 271ms\n",
      "99:\tlearn: 0.0967791\ttest: 0.0967670\tbest: 0.0967670 (99)\ttotal: 27.1s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.0967669841\n",
      "bestIteration = 99\n",
      "\n",
      "5:\tloss: 0.0967670\tbest: 0.0967670 (5)\ttotal: 2m 40s\tremaining: 4m 26s\n",
      "0:\tlearn: 0.5479383\ttest: 0.5483108\tbest: 0.5483108 (0)\ttotal: 310ms\tremaining: 46.2s\n",
      "1:\tlearn: 0.5327330\ttest: 0.5330974\tbest: 0.5330974 (1)\ttotal: 634ms\tremaining: 46.9s\n",
      "2:\tlearn: 0.5180603\ttest: 0.5184155\tbest: 0.5184155 (2)\ttotal: 912ms\tremaining: 44.7s\n",
      "3:\tlearn: 0.5037982\ttest: 0.5041447\tbest: 0.5041447 (3)\ttotal: 1.19s\tremaining: 43.4s\n",
      "4:\tlearn: 0.4900053\ttest: 0.4903470\tbest: 0.4903470 (4)\ttotal: 1.46s\tremaining: 42.4s\n",
      "5:\tlearn: 0.4766645\ttest: 0.4770000\tbest: 0.4770000 (5)\ttotal: 1.73s\tremaining: 41.6s\n",
      "6:\tlearn: 0.4637524\ttest: 0.4640810\tbest: 0.4640810 (6)\ttotal: 2.02s\tremaining: 41.3s\n",
      "7:\tlearn: 0.4512377\ttest: 0.4515593\tbest: 0.4515593 (7)\ttotal: 2.31s\tremaining: 41s\n",
      "8:\tlearn: 0.4390881\ttest: 0.4394030\tbest: 0.4394030 (8)\ttotal: 2.59s\tremaining: 40.6s\n",
      "9:\tlearn: 0.4273610\ttest: 0.4276710\tbest: 0.4276710 (9)\ttotal: 2.88s\tremaining: 40.3s\n",
      "10:\tlearn: 0.4160194\ttest: 0.4163236\tbest: 0.4163236 (10)\ttotal: 3.16s\tremaining: 39.9s\n",
      "11:\tlearn: 0.4050653\ttest: 0.4053647\tbest: 0.4053647 (11)\ttotal: 3.43s\tremaining: 39.4s\n",
      "12:\tlearn: 0.3944479\ttest: 0.3947392\tbest: 0.3947392 (12)\ttotal: 3.71s\tremaining: 39.1s\n",
      "13:\tlearn: 0.3841598\ttest: 0.3844442\tbest: 0.3844442 (13)\ttotal: 3.99s\tremaining: 38.8s\n",
      "14:\tlearn: 0.3742468\ttest: 0.3745265\tbest: 0.3745265 (14)\ttotal: 4.29s\tremaining: 38.6s\n",
      "15:\tlearn: 0.3646277\ttest: 0.3649016\tbest: 0.3649016 (15)\ttotal: 4.58s\tremaining: 38.4s\n",
      "16:\tlearn: 0.3553219\ttest: 0.3555889\tbest: 0.3555889 (16)\ttotal: 4.85s\tremaining: 37.9s\n",
      "17:\tlearn: 0.3463447\ttest: 0.3466063\tbest: 0.3466063 (17)\ttotal: 5.12s\tremaining: 37.6s\n",
      "18:\tlearn: 0.3377139\ttest: 0.3379723\tbest: 0.3379723 (18)\ttotal: 5.41s\tremaining: 37.3s\n",
      "19:\tlearn: 0.3292991\ttest: 0.3295517\tbest: 0.3295517 (19)\ttotal: 5.68s\tremaining: 36.9s\n",
      "20:\tlearn: 0.3211741\ttest: 0.3214212\tbest: 0.3214212 (20)\ttotal: 5.96s\tremaining: 36.6s\n",
      "21:\tlearn: 0.3133576\ttest: 0.3136002\tbest: 0.3136002 (21)\ttotal: 6.24s\tremaining: 36.3s\n",
      "22:\tlearn: 0.3057991\ttest: 0.3060369\tbest: 0.3060369 (22)\ttotal: 6.53s\tremaining: 36s\n",
      "23:\tlearn: 0.2984885\ttest: 0.2987212\tbest: 0.2987212 (23)\ttotal: 6.82s\tremaining: 35.8s\n",
      "24:\tlearn: 0.2914361\ttest: 0.2916624\tbest: 0.2916624 (24)\ttotal: 7.11s\tremaining: 35.5s\n",
      "25:\tlearn: 0.2846297\ttest: 0.2848525\tbest: 0.2848525 (25)\ttotal: 7.4s\tremaining: 35.3s\n",
      "26:\tlearn: 0.2780623\ttest: 0.2782831\tbest: 0.2782831 (26)\ttotal: 7.67s\tremaining: 34.9s\n",
      "27:\tlearn: 0.2717128\ttest: 0.2719290\tbest: 0.2719290 (27)\ttotal: 7.96s\tremaining: 34.7s\n",
      "28:\tlearn: 0.2655919\ttest: 0.2658037\tbest: 0.2658037 (28)\ttotal: 8.26s\tremaining: 34.5s\n",
      "29:\tlearn: 0.2597185\ttest: 0.2599268\tbest: 0.2599268 (29)\ttotal: 8.53s\tremaining: 34.1s\n",
      "30:\tlearn: 0.2540615\ttest: 0.2542664\tbest: 0.2542664 (30)\ttotal: 8.8s\tremaining: 33.8s\n",
      "31:\tlearn: 0.2485315\ttest: 0.2487339\tbest: 0.2487339 (31)\ttotal: 9.08s\tremaining: 33.5s\n",
      "32:\tlearn: 0.2432815\ttest: 0.2434852\tbest: 0.2434852 (32)\ttotal: 9.35s\tremaining: 33.2s\n",
      "33:\tlearn: 0.2381752\ttest: 0.2383743\tbest: 0.2383743 (33)\ttotal: 9.63s\tremaining: 32.9s\n",
      "34:\tlearn: 0.2332250\ttest: 0.2334169\tbest: 0.2334169 (34)\ttotal: 9.92s\tremaining: 32.6s\n",
      "35:\tlearn: 0.2284476\ttest: 0.2286341\tbest: 0.2286341 (35)\ttotal: 10.2s\tremaining: 32.3s\n",
      "36:\tlearn: 0.2238389\ttest: 0.2240179\tbest: 0.2240179 (36)\ttotal: 10.5s\tremaining: 32.2s\n",
      "37:\tlearn: 0.2194540\ttest: 0.2196303\tbest: 0.2196303 (37)\ttotal: 10.8s\tremaining: 31.8s\n",
      "38:\tlearn: 0.2152537\ttest: 0.2154281\tbest: 0.2154281 (38)\ttotal: 11.1s\tremaining: 31.5s\n",
      "39:\tlearn: 0.2111402\ttest: 0.2113086\tbest: 0.2113086 (39)\ttotal: 11.4s\tremaining: 31.2s\n",
      "40:\tlearn: 0.2071880\ttest: 0.2073491\tbest: 0.2073491 (40)\ttotal: 11.7s\tremaining: 31s\n",
      "41:\tlearn: 0.2034034\ttest: 0.2035631\tbest: 0.2035631 (41)\ttotal: 11.9s\tremaining: 30.7s\n",
      "42:\tlearn: 0.1996319\ttest: 0.1997904\tbest: 0.1997904 (42)\ttotal: 12.2s\tremaining: 30.4s\n",
      "43:\tlearn: 0.1961421\ttest: 0.1962983\tbest: 0.1962983 (43)\ttotal: 12.5s\tremaining: 30.1s\n",
      "44:\tlearn: 0.1927747\ttest: 0.1929290\tbest: 0.1929290 (44)\ttotal: 12.8s\tremaining: 29.8s\n",
      "45:\tlearn: 0.1894201\ttest: 0.1895721\tbest: 0.1895721 (45)\ttotal: 13.1s\tremaining: 29.6s\n",
      "46:\tlearn: 0.1863413\ttest: 0.1864913\tbest: 0.1864913 (46)\ttotal: 13.4s\tremaining: 29.3s\n",
      "47:\tlearn: 0.1833548\ttest: 0.1835005\tbest: 0.1835005 (47)\ttotal: 13.6s\tremaining: 28.9s\n",
      "48:\tlearn: 0.1804557\ttest: 0.1805950\tbest: 0.1805950 (48)\ttotal: 13.9s\tremaining: 28.7s\n",
      "49:\tlearn: 0.1776735\ttest: 0.1778071\tbest: 0.1778071 (49)\ttotal: 14.2s\tremaining: 28.4s\n",
      "50:\tlearn: 0.1750152\ttest: 0.1751489\tbest: 0.1751489 (50)\ttotal: 14.5s\tremaining: 28.2s\n",
      "51:\tlearn: 0.1724860\ttest: 0.1726160\tbest: 0.1726160 (51)\ttotal: 14.8s\tremaining: 27.9s\n",
      "52:\tlearn: 0.1700367\ttest: 0.1701604\tbest: 0.1701604 (52)\ttotal: 15.1s\tremaining: 27.6s\n",
      "53:\tlearn: 0.1675732\ttest: 0.1676939\tbest: 0.1676939 (53)\ttotal: 15.4s\tremaining: 27.3s\n",
      "54:\tlearn: 0.1653115\ttest: 0.1654281\tbest: 0.1654281 (54)\ttotal: 15.6s\tremaining: 27s\n",
      "55:\tlearn: 0.1630557\ttest: 0.1631705\tbest: 0.1631705 (55)\ttotal: 15.9s\tremaining: 26.7s\n",
      "56:\tlearn: 0.1609823\ttest: 0.1610915\tbest: 0.1610915 (56)\ttotal: 16.2s\tremaining: 26.5s\n",
      "57:\tlearn: 0.1589668\ttest: 0.1590709\tbest: 0.1590709 (57)\ttotal: 16.5s\tremaining: 26.2s\n",
      "58:\tlearn: 0.1570551\ttest: 0.1571536\tbest: 0.1571536 (58)\ttotal: 16.8s\tremaining: 25.9s\n",
      "59:\tlearn: 0.1552328\ttest: 0.1553257\tbest: 0.1553257 (59)\ttotal: 17.1s\tremaining: 25.6s\n",
      "60:\tlearn: 0.1533662\ttest: 0.1534570\tbest: 0.1534570 (60)\ttotal: 17.4s\tremaining: 25.3s\n",
      "61:\tlearn: 0.1516498\ttest: 0.1517361\tbest: 0.1517361 (61)\ttotal: 17.6s\tremaining: 25s\n",
      "62:\tlearn: 0.1500526\ttest: 0.1501342\tbest: 0.1501342 (62)\ttotal: 17.9s\tremaining: 24.7s\n",
      "63:\tlearn: 0.1484182\ttest: 0.1484967\tbest: 0.1484967 (63)\ttotal: 18.2s\tremaining: 24.5s\n",
      "64:\tlearn: 0.1469270\ttest: 0.1470026\tbest: 0.1470026 (64)\ttotal: 18.5s\tremaining: 24.2s\n",
      "65:\tlearn: 0.1455293\ttest: 0.1456003\tbest: 0.1456003 (65)\ttotal: 18.8s\tremaining: 23.9s\n",
      "66:\tlearn: 0.1442002\ttest: 0.1442666\tbest: 0.1442666 (66)\ttotal: 19s\tremaining: 23.6s\n",
      "67:\tlearn: 0.1429005\ttest: 0.1429619\tbest: 0.1429619 (67)\ttotal: 19.3s\tremaining: 23.3s\n",
      "68:\tlearn: 0.1416355\ttest: 0.1416926\tbest: 0.1416926 (68)\ttotal: 19.6s\tremaining: 23s\n",
      "69:\tlearn: 0.1404363\ttest: 0.1404892\tbest: 0.1404892 (69)\ttotal: 19.9s\tremaining: 22.7s\n",
      "70:\tlearn: 0.1392832\ttest: 0.1393309\tbest: 0.1393309 (70)\ttotal: 20.1s\tremaining: 22.4s\n",
      "71:\tlearn: 0.1381982\ttest: 0.1382418\tbest: 0.1382418 (71)\ttotal: 20.5s\tremaining: 22.2s\n",
      "72:\tlearn: 0.1371771\ttest: 0.1372180\tbest: 0.1372180 (72)\ttotal: 20.8s\tremaining: 21.9s\n",
      "73:\tlearn: 0.1361892\ttest: 0.1362268\tbest: 0.1362268 (73)\ttotal: 21s\tremaining: 21.6s\n",
      "74:\tlearn: 0.1352440\ttest: 0.1352772\tbest: 0.1352772 (74)\ttotal: 21.3s\tremaining: 21.3s\n",
      "75:\tlearn: 0.1343486\ttest: 0.1343778\tbest: 0.1343778 (75)\ttotal: 21.6s\tremaining: 21s\n",
      "76:\tlearn: 0.1334724\ttest: 0.1334991\tbest: 0.1334991 (76)\ttotal: 21.9s\tremaining: 20.7s\n",
      "77:\tlearn: 0.1326487\ttest: 0.1326726\tbest: 0.1326726 (77)\ttotal: 22.1s\tremaining: 20.4s\n",
      "78:\tlearn: 0.1316902\ttest: 0.1317115\tbest: 0.1317115 (78)\ttotal: 22.4s\tremaining: 20.2s\n",
      "79:\tlearn: 0.1308884\ttest: 0.1309085\tbest: 0.1309085 (79)\ttotal: 22.7s\tremaining: 19.9s\n",
      "80:\tlearn: 0.1301325\ttest: 0.1301513\tbest: 0.1301513 (80)\ttotal: 23s\tremaining: 19.6s\n",
      "81:\tlearn: 0.1294368\ttest: 0.1294520\tbest: 0.1294520 (81)\ttotal: 23.2s\tremaining: 19.3s\n",
      "82:\tlearn: 0.1286166\ttest: 0.1286299\tbest: 0.1286299 (82)\ttotal: 23.5s\tremaining: 19s\n",
      "83:\tlearn: 0.1279498\ttest: 0.1279595\tbest: 0.1279595 (83)\ttotal: 23.8s\tremaining: 18.7s\n",
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      "85:\tlearn: 0.1266679\ttest: 0.1266733\tbest: 0.1266733 (85)\ttotal: 24.3s\tremaining: 18.1s\n",
      "86:\tlearn: 0.1260717\ttest: 0.1260762\tbest: 0.1260762 (86)\ttotal: 24.6s\tremaining: 17.8s\n",
      "87:\tlearn: 0.1255138\ttest: 0.1255160\tbest: 0.1255160 (87)\ttotal: 24.9s\tremaining: 17.5s\n",
      "88:\tlearn: 0.1249902\ttest: 0.1249919\tbest: 0.1249919 (88)\ttotal: 25.2s\tremaining: 17.2s\n",
      "89:\tlearn: 0.1244859\ttest: 0.1244847\tbest: 0.1244847 (89)\ttotal: 25.4s\tremaining: 16.9s\n",
      "90:\tlearn: 0.1240077\ttest: 0.1240066\tbest: 0.1240066 (90)\ttotal: 25.6s\tremaining: 16.6s\n",
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      "94:\tlearn: 0.1222340\ttest: 0.1222244\tbest: 0.1222244 (94)\ttotal: 26.8s\tremaining: 15.5s\n",
      "95:\tlearn: 0.1217686\ttest: 0.1217575\tbest: 0.1217575 (95)\ttotal: 27.1s\tremaining: 15.2s\n",
      "96:\tlearn: 0.1213581\ttest: 0.1213435\tbest: 0.1213435 (96)\ttotal: 27.5s\tremaining: 15s\n",
      "97:\tlearn: 0.1209920\ttest: 0.1209759\tbest: 0.1209759 (97)\ttotal: 27.8s\tremaining: 14.8s\n",
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      "99:\tlearn: 0.1201525\ttest: 0.1201343\tbest: 0.1201343 (99)\ttotal: 28.5s\tremaining: 14.3s\n",
      "100:\tlearn: 0.1198330\ttest: 0.1198126\tbest: 0.1198126 (100)\ttotal: 28.9s\tremaining: 14s\n",
      "101:\tlearn: 0.1194002\ttest: 0.1193792\tbest: 0.1193792 (101)\ttotal: 29.2s\tremaining: 13.7s\n",
      "102:\tlearn: 0.1190805\ttest: 0.1190585\tbest: 0.1190585 (102)\ttotal: 29.4s\tremaining: 13.4s\n",
      "103:\tlearn: 0.1187618\ttest: 0.1187401\tbest: 0.1187401 (103)\ttotal: 29.7s\tremaining: 13.1s\n",
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      "105:\tlearn: 0.1181313\ttest: 0.1181048\tbest: 0.1181048 (105)\ttotal: 30.2s\tremaining: 12.5s\n",
      "106:\tlearn: 0.1178496\ttest: 0.1178234\tbest: 0.1178234 (106)\ttotal: 30.5s\tremaining: 12.2s\n",
      "107:\tlearn: 0.1174860\ttest: 0.1174572\tbest: 0.1174572 (107)\ttotal: 30.8s\tremaining: 12s\n",
      "108:\tlearn: 0.1170926\ttest: 0.1170616\tbest: 0.1170616 (108)\ttotal: 31s\tremaining: 11.7s\n",
      "109:\tlearn: 0.1168409\ttest: 0.1168090\tbest: 0.1168090 (109)\ttotal: 31.3s\tremaining: 11.4s\n",
      "110:\tlearn: 0.1165136\ttest: 0.1164822\tbest: 0.1164822 (110)\ttotal: 31.6s\tremaining: 11.1s\n",
      "111:\tlearn: 0.1162886\ttest: 0.1162564\tbest: 0.1162564 (111)\ttotal: 31.8s\tremaining: 10.8s\n",
      "112:\tlearn: 0.1160645\ttest: 0.1160315\tbest: 0.1160315 (112)\ttotal: 32s\tremaining: 10.5s\n",
      "113:\tlearn: 0.1157610\ttest: 0.1157275\tbest: 0.1157275 (113)\ttotal: 32.3s\tremaining: 10.2s\n",
      "114:\tlearn: 0.1155298\ttest: 0.1154948\tbest: 0.1154948 (114)\ttotal: 32.6s\tremaining: 9.91s\n",
      "115:\tlearn: 0.1153323\ttest: 0.1152959\tbest: 0.1152959 (115)\ttotal: 32.8s\tremaining: 9.62s\n",
      "116:\tlearn: 0.1151380\ttest: 0.1151002\tbest: 0.1151002 (116)\ttotal: 33.1s\tremaining: 9.32s\n",
      "117:\tlearn: 0.1149437\ttest: 0.1149065\tbest: 0.1149065 (117)\ttotal: 33.3s\tremaining: 9.03s\n",
      "118:\tlearn: 0.1147533\ttest: 0.1147155\tbest: 0.1147155 (118)\ttotal: 33.5s\tremaining: 8.74s\n",
      "119:\tlearn: 0.1145758\ttest: 0.1145374\tbest: 0.1145374 (119)\ttotal: 33.8s\tremaining: 8.45s\n",
      "120:\tlearn: 0.1143489\ttest: 0.1143116\tbest: 0.1143116 (120)\ttotal: 34.1s\tremaining: 8.17s\n",
      "121:\tlearn: 0.1141799\ttest: 0.1141435\tbest: 0.1141435 (121)\ttotal: 34.3s\tremaining: 7.87s\n",
      "122:\tlearn: 0.1140186\ttest: 0.1139809\tbest: 0.1139809 (122)\ttotal: 34.6s\tremaining: 7.59s\n",
      "123:\tlearn: 0.1138375\ttest: 0.1137978\tbest: 0.1137978 (123)\ttotal: 34.8s\tremaining: 7.3s\n",
      "124:\tlearn: 0.1136781\ttest: 0.1136373\tbest: 0.1136373 (124)\ttotal: 35.1s\tremaining: 7.01s\n",
      "125:\tlearn: 0.1135046\ttest: 0.1134628\tbest: 0.1134628 (125)\ttotal: 35.3s\tremaining: 6.73s\n",
      "126:\tlearn: 0.1133399\ttest: 0.1133004\tbest: 0.1133004 (126)\ttotal: 35.6s\tremaining: 6.44s\n",
      "127:\tlearn: 0.1130963\ttest: 0.1130563\tbest: 0.1130563 (127)\ttotal: 35.8s\tremaining: 6.16s\n",
      "128:\tlearn: 0.1129050\ttest: 0.1128629\tbest: 0.1128629 (128)\ttotal: 36.1s\tremaining: 5.88s\n",
      "129:\tlearn: 0.1127593\ttest: 0.1127162\tbest: 0.1127162 (129)\ttotal: 36.4s\tremaining: 5.6s\n",
      "130:\tlearn: 0.1126208\ttest: 0.1125795\tbest: 0.1125795 (130)\ttotal: 36.6s\tremaining: 5.31s\n",
      "131:\tlearn: 0.1124742\ttest: 0.1124317\tbest: 0.1124317 (131)\ttotal: 36.9s\tremaining: 5.03s\n",
      "132:\tlearn: 0.1123521\ttest: 0.1123110\tbest: 0.1123110 (132)\ttotal: 37.2s\tremaining: 4.75s\n",
      "133:\tlearn: 0.1122349\ttest: 0.1121926\tbest: 0.1121926 (133)\ttotal: 37.4s\tremaining: 4.47s\n",
      "134:\tlearn: 0.1121057\ttest: 0.1120630\tbest: 0.1120630 (134)\ttotal: 37.6s\tremaining: 4.18s\n",
      "135:\tlearn: 0.1118409\ttest: 0.1117964\tbest: 0.1117964 (135)\ttotal: 38s\tremaining: 3.91s\n",
      "136:\tlearn: 0.1117228\ttest: 0.1116800\tbest: 0.1116800 (136)\ttotal: 38.2s\tremaining: 3.62s\n",
      "137:\tlearn: 0.1115240\ttest: 0.1114842\tbest: 0.1114842 (137)\ttotal: 38.4s\tremaining: 3.34s\n",
      "138:\tlearn: 0.1112563\ttest: 0.1112204\tbest: 0.1112204 (138)\ttotal: 38.7s\tremaining: 3.06s\n",
      "139:\tlearn: 0.1111466\ttest: 0.1111106\tbest: 0.1111106 (139)\ttotal: 39s\tremaining: 2.78s\n",
      "140:\tlearn: 0.1109409\ttest: 0.1109041\tbest: 0.1109041 (140)\ttotal: 39.2s\tremaining: 2.5s\n",
      "141:\tlearn: 0.1108418\ttest: 0.1108047\tbest: 0.1108047 (141)\ttotal: 39.5s\tremaining: 2.22s\n",
      "142:\tlearn: 0.1107040\ttest: 0.1106664\tbest: 0.1106664 (142)\ttotal: 39.7s\tremaining: 1.94s\n",
      "143:\tlearn: 0.1105892\ttest: 0.1105509\tbest: 0.1105509 (143)\ttotal: 40s\tremaining: 1.67s\n",
      "144:\tlearn: 0.1103877\ttest: 0.1103490\tbest: 0.1103490 (144)\ttotal: 40.3s\tremaining: 1.39s\n",
      "145:\tlearn: 0.1102628\ttest: 0.1102227\tbest: 0.1102227 (145)\ttotal: 40.6s\tremaining: 1.11s\n",
      "146:\tlearn: 0.1101680\ttest: 0.1101285\tbest: 0.1101285 (146)\ttotal: 40.8s\tremaining: 832ms\n",
      "147:\tlearn: 0.1099434\ttest: 0.1099081\tbest: 0.1099081 (147)\ttotal: 41.1s\tremaining: 555ms\n",
      "148:\tlearn: 0.1097605\ttest: 0.1097245\tbest: 0.1097245 (148)\ttotal: 41.4s\tremaining: 278ms\n",
      "149:\tlearn: 0.1095840\ttest: 0.1095488\tbest: 0.1095488 (149)\ttotal: 41.6s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.1095487618\n",
      "bestIteration = 149\n",
      "\n",
      "6:\tloss: 0.1095488\tbest: 0.0967670 (5)\ttotal: 3m 22s\tremaining: 4m 19s\n",
      "0:\tlearn: 0.5114392\ttest: 0.5117850\tbest: 0.5117850 (0)\ttotal: 301ms\tremaining: 44.8s\n",
      "1:\tlearn: 0.4648203\ttest: 0.4651492\tbest: 0.4651492 (1)\ttotal: 567ms\tremaining: 42s\n",
      "2:\tlearn: 0.4230285\ttest: 0.4233284\tbest: 0.4233284 (2)\ttotal: 845ms\tremaining: 41.4s\n",
      "3:\tlearn: 0.3859195\ttest: 0.3862066\tbest: 0.3862066 (3)\ttotal: 1.11s\tremaining: 40.4s\n",
      "4:\tlearn: 0.3528177\ttest: 0.3530876\tbest: 0.3530876 (4)\ttotal: 1.37s\tremaining: 39.8s\n",
      "5:\tlearn: 0.3234555\ttest: 0.3237088\tbest: 0.3237088 (5)\ttotal: 1.64s\tremaining: 39.3s\n",
      "6:\tlearn: 0.2973212\ttest: 0.2975520\tbest: 0.2975520 (6)\ttotal: 1.92s\tremaining: 39.3s\n",
      "7:\tlearn: 0.2742760\ttest: 0.2744923\tbest: 0.2744923 (7)\ttotal: 2.21s\tremaining: 39.2s\n",
      "8:\tlearn: 0.2538644\ttest: 0.2540683\tbest: 0.2540683 (8)\ttotal: 2.47s\tremaining: 38.7s\n",
      "9:\tlearn: 0.2359040\ttest: 0.2360988\tbest: 0.2360988 (9)\ttotal: 2.74s\tremaining: 38.3s\n",
      "10:\tlearn: 0.2200095\ttest: 0.2201863\tbest: 0.2201863 (10)\ttotal: 3.03s\tremaining: 38.3s\n",
      "11:\tlearn: 0.2062534\ttest: 0.2064192\tbest: 0.2064192 (11)\ttotal: 3.29s\tremaining: 37.9s\n",
      "12:\tlearn: 0.1939760\ttest: 0.1941195\tbest: 0.1941195 (12)\ttotal: 3.58s\tremaining: 37.7s\n",
      "13:\tlearn: 0.1833362\ttest: 0.1834613\tbest: 0.1834613 (13)\ttotal: 3.88s\tremaining: 37.7s\n",
      "14:\tlearn: 0.1740155\ttest: 0.1741211\tbest: 0.1741211 (14)\ttotal: 4.18s\tremaining: 37.6s\n",
      "15:\tlearn: 0.1659856\ttest: 0.1660719\tbest: 0.1660719 (15)\ttotal: 4.46s\tremaining: 37.3s\n",
      "16:\tlearn: 0.1590518\ttest: 0.1591195\tbest: 0.1591195 (16)\ttotal: 4.73s\tremaining: 37s\n",
      "17:\tlearn: 0.1532044\ttest: 0.1532553\tbest: 0.1532553 (17)\ttotal: 5.03s\tremaining: 36.9s\n",
      "18:\tlearn: 0.1480813\ttest: 0.1481186\tbest: 0.1481186 (18)\ttotal: 5.31s\tremaining: 36.6s\n",
      "19:\tlearn: 0.1437697\ttest: 0.1437969\tbest: 0.1437969 (19)\ttotal: 5.58s\tremaining: 36.3s\n",
      "20:\tlearn: 0.1393248\ttest: 0.1393409\tbest: 0.1393409 (20)\ttotal: 5.86s\tremaining: 36s\n",
      "21:\tlearn: 0.1361708\ttest: 0.1361760\tbest: 0.1361760 (21)\ttotal: 6.13s\tremaining: 35.7s\n",
      "22:\tlearn: 0.1332519\ttest: 0.1332451\tbest: 0.1332451 (22)\ttotal: 6.41s\tremaining: 35.4s\n",
      "23:\tlearn: 0.1309475\ttest: 0.1309418\tbest: 0.1309418 (23)\ttotal: 6.66s\tremaining: 34.9s\n",
      "24:\tlearn: 0.1288058\ttest: 0.1287958\tbest: 0.1287958 (24)\ttotal: 6.95s\tremaining: 34.7s\n",
      "25:\tlearn: 0.1264772\ttest: 0.1264589\tbest: 0.1264589 (25)\ttotal: 7.23s\tremaining: 34.5s\n",
      "26:\tlearn: 0.1248442\ttest: 0.1248177\tbest: 0.1248177 (26)\ttotal: 7.48s\tremaining: 34.1s\n",
      "27:\tlearn: 0.1233962\ttest: 0.1233607\tbest: 0.1233607 (27)\ttotal: 7.77s\tremaining: 33.8s\n",
      "28:\tlearn: 0.1217939\ttest: 0.1217505\tbest: 0.1217505 (28)\ttotal: 8.05s\tremaining: 33.6s\n",
      "29:\tlearn: 0.1203213\ttest: 0.1202784\tbest: 0.1202784 (29)\ttotal: 8.32s\tremaining: 33.3s\n",
      "30:\tlearn: 0.1193770\ttest: 0.1193300\tbest: 0.1193300 (30)\ttotal: 8.6s\tremaining: 33s\n",
      "31:\tlearn: 0.1185694\ttest: 0.1185169\tbest: 0.1185169 (31)\ttotal: 8.85s\tremaining: 32.6s\n",
      "32:\tlearn: 0.1178179\ttest: 0.1177626\tbest: 0.1177626 (32)\ttotal: 9.12s\tremaining: 32.3s\n",
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      "34:\tlearn: 0.1164810\ttest: 0.1164286\tbest: 0.1164286 (34)\ttotal: 9.61s\tremaining: 31.6s\n",
      "35:\tlearn: 0.1151104\ttest: 0.1150628\tbest: 0.1150628 (35)\ttotal: 9.93s\tremaining: 31.4s\n",
      "36:\tlearn: 0.1145724\ttest: 0.1145228\tbest: 0.1145228 (36)\ttotal: 10.2s\tremaining: 31.1s\n",
      "37:\tlearn: 0.1140529\ttest: 0.1139975\tbest: 0.1139975 (37)\ttotal: 10.5s\tremaining: 30.8s\n",
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      "40:\tlearn: 0.1124156\ttest: 0.1123699\tbest: 0.1123699 (40)\ttotal: 11.2s\tremaining: 29.7s\n",
      "41:\tlearn: 0.1119943\ttest: 0.1119438\tbest: 0.1119438 (41)\ttotal: 11.4s\tremaining: 29.4s\n",
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      "56:\tlearn: 0.1055557\ttest: 0.1055056\tbest: 0.1055056 (56)\ttotal: 15.4s\tremaining: 25.2s\n",
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      "59:\tlearn: 0.1046258\ttest: 0.1045715\tbest: 0.1045715 (59)\ttotal: 16.2s\tremaining: 24.2s\n",
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      "120:\tlearn: 0.0941790\ttest: 0.0941820\tbest: 0.0941820 (120)\ttotal: 32.3s\tremaining: 7.74s\n",
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      "126:\tlearn: 0.0934419\ttest: 0.0934517\tbest: 0.0934517 (126)\ttotal: 33.9s\tremaining: 6.14s\n",
      "127:\tlearn: 0.0933973\ttest: 0.0934092\tbest: 0.0934092 (127)\ttotal: 34.1s\tremaining: 5.86s\n",
      "128:\tlearn: 0.0932596\ttest: 0.0932740\tbest: 0.0932740 (128)\ttotal: 34.4s\tremaining: 5.6s\n",
      "129:\tlearn: 0.0931292\ttest: 0.0931420\tbest: 0.0931420 (129)\ttotal: 34.6s\tremaining: 5.33s\n",
      "130:\tlearn: 0.0929891\ttest: 0.0930036\tbest: 0.0930036 (130)\ttotal: 34.9s\tremaining: 5.06s\n",
      "131:\tlearn: 0.0928490\ttest: 0.0928663\tbest: 0.0928663 (131)\ttotal: 35.2s\tremaining: 4.79s\n",
      "132:\tlearn: 0.0928065\ttest: 0.0928230\tbest: 0.0928230 (132)\ttotal: 35.4s\tremaining: 4.52s\n",
      "133:\tlearn: 0.0927654\ttest: 0.0927830\tbest: 0.0927830 (133)\ttotal: 35.6s\tremaining: 4.25s\n",
      "134:\tlearn: 0.0926476\ttest: 0.0926643\tbest: 0.0926643 (134)\ttotal: 35.9s\tremaining: 3.98s\n",
      "135:\tlearn: 0.0926034\ttest: 0.0926196\tbest: 0.0926196 (135)\ttotal: 36.1s\tremaining: 3.72s\n",
      "136:\tlearn: 0.0924851\ttest: 0.0925031\tbest: 0.0925031 (136)\ttotal: 36.4s\tremaining: 3.45s\n",
      "137:\tlearn: 0.0924178\ttest: 0.0924362\tbest: 0.0924362 (137)\ttotal: 36.7s\tremaining: 3.19s\n",
      "138:\tlearn: 0.0923067\ttest: 0.0923271\tbest: 0.0923271 (138)\ttotal: 37s\tremaining: 2.92s\n",
      "139:\tlearn: 0.0921722\ttest: 0.0921935\tbest: 0.0921935 (139)\ttotal: 37.2s\tremaining: 2.66s\n",
      "140:\tlearn: 0.0920596\ttest: 0.0920794\tbest: 0.0920794 (140)\ttotal: 37.5s\tremaining: 2.39s\n",
      "141:\tlearn: 0.0920044\ttest: 0.0920242\tbest: 0.0920242 (141)\ttotal: 37.8s\tremaining: 2.13s\n",
      "142:\tlearn: 0.0918917\ttest: 0.0919123\tbest: 0.0919123 (142)\ttotal: 38s\tremaining: 1.86s\n",
      "143:\tlearn: 0.0918149\ttest: 0.0918356\tbest: 0.0918356 (143)\ttotal: 38.3s\tremaining: 1.59s\n",
      "144:\tlearn: 0.0916760\ttest: 0.0916969\tbest: 0.0916969 (144)\ttotal: 38.6s\tremaining: 1.33s\n",
      "145:\tlearn: 0.0915199\ttest: 0.0915365\tbest: 0.0915365 (145)\ttotal: 38.8s\tremaining: 1.06s\n",
      "146:\tlearn: 0.0914810\ttest: 0.0914963\tbest: 0.0914963 (146)\ttotal: 39s\tremaining: 797ms\n",
      "147:\tlearn: 0.0913562\ttest: 0.0913671\tbest: 0.0913671 (147)\ttotal: 39.3s\tremaining: 531ms\n",
      "148:\tlearn: 0.0912701\ttest: 0.0912798\tbest: 0.0912798 (148)\ttotal: 39.6s\tremaining: 266ms\n",
      "149:\tlearn: 0.0911831\ttest: 0.0911950\tbest: 0.0911950 (149)\ttotal: 39.9s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.09119503565\n",
      "bestIteration = 149\n",
      "\n",
      "7:\tloss: 0.0911950\tbest: 0.0911950 (7)\ttotal: 4m 2s\tremaining: 4m 2s\n",
      "0:\tlearn: 0.5477899\ttest: 0.5481642\tbest: 0.5481642 (0)\ttotal: 388ms\tremaining: 38.4s\n",
      "1:\tlearn: 0.5324462\ttest: 0.5328088\tbest: 0.5328088 (1)\ttotal: 755ms\tremaining: 37s\n",
      "2:\tlearn: 0.5175868\ttest: 0.5179442\tbest: 0.5179442 (2)\ttotal: 1.12s\tremaining: 36.2s\n",
      "3:\tlearn: 0.5032060\ttest: 0.5035516\tbest: 0.5035516 (3)\ttotal: 1.48s\tremaining: 35.6s\n",
      "4:\tlearn: 0.4892458\ttest: 0.4895833\tbest: 0.4895833 (4)\ttotal: 1.84s\tremaining: 34.9s\n",
      "5:\tlearn: 0.4757004\ttest: 0.4760336\tbest: 0.4760336 (5)\ttotal: 2.21s\tremaining: 34.7s\n",
      "6:\tlearn: 0.4625569\ttest: 0.4628842\tbest: 0.4628842 (6)\ttotal: 2.57s\tremaining: 34.2s\n",
      "7:\tlearn: 0.4498186\ttest: 0.4501373\tbest: 0.4501373 (7)\ttotal: 2.94s\tremaining: 33.8s\n",
      "8:\tlearn: 0.4375075\ttest: 0.4378156\tbest: 0.4378156 (8)\ttotal: 3.3s\tremaining: 33.3s\n",
      "9:\tlearn: 0.4255753\ttest: 0.4258785\tbest: 0.4258785 (9)\ttotal: 3.66s\tremaining: 32.9s\n",
      "10:\tlearn: 0.4140739\ttest: 0.4143693\tbest: 0.4143693 (10)\ttotal: 4.03s\tremaining: 32.6s\n",
      "11:\tlearn: 0.4029526\ttest: 0.4032388\tbest: 0.4032388 (11)\ttotal: 4.38s\tremaining: 32.1s\n",
      "12:\tlearn: 0.3921416\ttest: 0.3924243\tbest: 0.3924243 (12)\ttotal: 4.74s\tremaining: 31.7s\n",
      "13:\tlearn: 0.3816650\ttest: 0.3819388\tbest: 0.3819388 (13)\ttotal: 5.1s\tremaining: 31.3s\n",
      "14:\tlearn: 0.3715609\ttest: 0.3718276\tbest: 0.3718276 (14)\ttotal: 5.46s\tremaining: 31s\n",
      "15:\tlearn: 0.3617383\ttest: 0.3620031\tbest: 0.3620031 (15)\ttotal: 5.84s\tremaining: 30.6s\n",
      "16:\tlearn: 0.3522771\ttest: 0.3525341\tbest: 0.3525341 (16)\ttotal: 6.21s\tremaining: 30.3s\n",
      "17:\tlearn: 0.3430697\ttest: 0.3433232\tbest: 0.3433232 (17)\ttotal: 6.58s\tremaining: 30s\n",
      "18:\tlearn: 0.3342020\ttest: 0.3344522\tbest: 0.3344522 (18)\ttotal: 6.95s\tremaining: 29.6s\n",
      "19:\tlearn: 0.3256181\ttest: 0.3258658\tbest: 0.3258658 (19)\ttotal: 7.31s\tremaining: 29.2s\n",
      "20:\tlearn: 0.3173265\ttest: 0.3175752\tbest: 0.3175752 (20)\ttotal: 7.68s\tremaining: 28.9s\n",
      "21:\tlearn: 0.3092722\ttest: 0.3095186\tbest: 0.3095186 (21)\ttotal: 8.05s\tremaining: 28.6s\n",
      "22:\tlearn: 0.3014931\ttest: 0.3017315\tbest: 0.3017315 (22)\ttotal: 8.46s\tremaining: 28.3s\n",
      "23:\tlearn: 0.2939661\ttest: 0.2942016\tbest: 0.2942016 (23)\ttotal: 8.83s\tremaining: 28s\n",
      "24:\tlearn: 0.2867152\ttest: 0.2869437\tbest: 0.2869437 (24)\ttotal: 9.19s\tremaining: 27.6s\n",
      "25:\tlearn: 0.2797200\ttest: 0.2799455\tbest: 0.2799455 (25)\ttotal: 9.64s\tremaining: 27.4s\n",
      "26:\tlearn: 0.2729910\ttest: 0.2732098\tbest: 0.2732098 (26)\ttotal: 10.1s\tremaining: 27.4s\n",
      "27:\tlearn: 0.2665095\ttest: 0.2667227\tbest: 0.2667227 (27)\ttotal: 10.6s\tremaining: 27.4s\n",
      "28:\tlearn: 0.2602353\ttest: 0.2604481\tbest: 0.2604481 (28)\ttotal: 11.1s\tremaining: 27.2s\n",
      "29:\tlearn: 0.2541400\ttest: 0.2543457\tbest: 0.2543457 (29)\ttotal: 11.5s\tremaining: 26.8s\n",
      "30:\tlearn: 0.2482918\ttest: 0.2484948\tbest: 0.2484948 (30)\ttotal: 11.8s\tremaining: 26.3s\n",
      "31:\tlearn: 0.2426497\ttest: 0.2428479\tbest: 0.2428479 (31)\ttotal: 12.2s\tremaining: 25.9s\n",
      "32:\tlearn: 0.2371625\ttest: 0.2373541\tbest: 0.2373541 (32)\ttotal: 12.6s\tremaining: 25.5s\n",
      "33:\tlearn: 0.2318764\ttest: 0.2320608\tbest: 0.2320608 (33)\ttotal: 13s\tremaining: 25.2s\n",
      "34:\tlearn: 0.2267976\ttest: 0.2269759\tbest: 0.2269759 (34)\ttotal: 13.3s\tremaining: 24.8s\n",
      "35:\tlearn: 0.2218714\ttest: 0.2220506\tbest: 0.2220506 (35)\ttotal: 13.7s\tremaining: 24.4s\n",
      "36:\tlearn: 0.2171223\ttest: 0.2172962\tbest: 0.2172962 (36)\ttotal: 14.1s\tremaining: 24s\n",
      "37:\tlearn: 0.2125409\ttest: 0.2127131\tbest: 0.2127131 (37)\ttotal: 14.5s\tremaining: 23.6s\n",
      "38:\tlearn: 0.2081376\ttest: 0.2083051\tbest: 0.2083051 (38)\ttotal: 14.8s\tremaining: 23.1s\n",
      "39:\tlearn: 0.2039086\ttest: 0.2040687\tbest: 0.2040687 (39)\ttotal: 15.2s\tremaining: 22.8s\n",
      "40:\tlearn: 0.1998037\ttest: 0.1999581\tbest: 0.1999581 (40)\ttotal: 15.5s\tremaining: 22.3s\n",
      "41:\tlearn: 0.1958906\ttest: 0.1960436\tbest: 0.1960436 (41)\ttotal: 15.9s\tremaining: 21.9s\n",
      "42:\tlearn: 0.1921140\ttest: 0.1922613\tbest: 0.1922613 (42)\ttotal: 16.3s\tremaining: 21.6s\n",
      "43:\tlearn: 0.1884418\ttest: 0.1885905\tbest: 0.1885905 (43)\ttotal: 16.6s\tremaining: 21.2s\n",
      "44:\tlearn: 0.1849495\ttest: 0.1850925\tbest: 0.1850925 (44)\ttotal: 17s\tremaining: 20.8s\n",
      "45:\tlearn: 0.1815623\ttest: 0.1817000\tbest: 0.1817000 (45)\ttotal: 17.3s\tremaining: 20.4s\n",
      "46:\tlearn: 0.1782995\ttest: 0.1784355\tbest: 0.1784355 (46)\ttotal: 17.7s\tremaining: 19.9s\n",
      "47:\tlearn: 0.1751966\ttest: 0.1753286\tbest: 0.1753286 (47)\ttotal: 18s\tremaining: 19.5s\n",
      "48:\tlearn: 0.1722258\ttest: 0.1723537\tbest: 0.1723537 (48)\ttotal: 18.4s\tremaining: 19.1s\n",
      "49:\tlearn: 0.1692636\ttest: 0.1693921\tbest: 0.1693921 (49)\ttotal: 18.8s\tremaining: 18.8s\n",
      "50:\tlearn: 0.1664554\ttest: 0.1665820\tbest: 0.1665820 (50)\ttotal: 19.2s\tremaining: 18.4s\n",
      "51:\tlearn: 0.1638216\ttest: 0.1639442\tbest: 0.1639442 (51)\ttotal: 19.5s\tremaining: 18s\n",
      "52:\tlearn: 0.1612496\ttest: 0.1613668\tbest: 0.1613668 (52)\ttotal: 19.9s\tremaining: 17.6s\n",
      "53:\tlearn: 0.1587804\ttest: 0.1588977\tbest: 0.1588977 (53)\ttotal: 20.3s\tremaining: 17.3s\n",
      "54:\tlearn: 0.1563738\ttest: 0.1564892\tbest: 0.1564892 (54)\ttotal: 20.7s\tremaining: 16.9s\n",
      "55:\tlearn: 0.1541158\ttest: 0.1542263\tbest: 0.1542263 (55)\ttotal: 21s\tremaining: 16.5s\n",
      "56:\tlearn: 0.1519308\ttest: 0.1520387\tbest: 0.1520387 (56)\ttotal: 21.4s\tremaining: 16.1s\n",
      "57:\tlearn: 0.1498505\ttest: 0.1499553\tbest: 0.1499553 (57)\ttotal: 21.7s\tremaining: 15.7s\n",
      "58:\tlearn: 0.1478581\ttest: 0.1479568\tbest: 0.1479568 (58)\ttotal: 22.1s\tremaining: 15.3s\n",
      "59:\tlearn: 0.1458972\ttest: 0.1459935\tbest: 0.1459935 (59)\ttotal: 22.4s\tremaining: 14.9s\n",
      "60:\tlearn: 0.1440727\ttest: 0.1441651\tbest: 0.1441651 (60)\ttotal: 22.8s\tremaining: 14.6s\n",
      "61:\tlearn: 0.1423098\ttest: 0.1423983\tbest: 0.1423983 (61)\ttotal: 23.2s\tremaining: 14.2s\n",
      "62:\tlearn: 0.1406208\ttest: 0.1407033\tbest: 0.1407033 (62)\ttotal: 23.6s\tremaining: 13.8s\n",
      "63:\tlearn: 0.1389612\ttest: 0.1390444\tbest: 0.1390444 (63)\ttotal: 23.9s\tremaining: 13.5s\n",
      "64:\tlearn: 0.1374102\ttest: 0.1374933\tbest: 0.1374933 (64)\ttotal: 24.3s\tremaining: 13.1s\n",
      "65:\tlearn: 0.1359361\ttest: 0.1360143\tbest: 0.1360143 (65)\ttotal: 24.6s\tremaining: 12.7s\n",
      "66:\tlearn: 0.1344344\ttest: 0.1345129\tbest: 0.1345129 (66)\ttotal: 25s\tremaining: 12.3s\n",
      "67:\tlearn: 0.1331165\ttest: 0.1331905\tbest: 0.1331905 (67)\ttotal: 25.4s\tremaining: 11.9s\n",
      "68:\tlearn: 0.1318228\ttest: 0.1318935\tbest: 0.1318935 (68)\ttotal: 25.7s\tremaining: 11.6s\n",
      "69:\tlearn: 0.1306020\ttest: 0.1306711\tbest: 0.1306711 (69)\ttotal: 26.1s\tremaining: 11.2s\n",
      "70:\tlearn: 0.1294182\ttest: 0.1294843\tbest: 0.1294843 (70)\ttotal: 26.5s\tremaining: 10.8s\n",
      "71:\tlearn: 0.1282928\ttest: 0.1283551\tbest: 0.1283551 (71)\ttotal: 26.9s\tremaining: 10.4s\n",
      "72:\tlearn: 0.1270933\ttest: 0.1271583\tbest: 0.1271583 (72)\ttotal: 27.2s\tremaining: 10.1s\n",
      "73:\tlearn: 0.1260687\ttest: 0.1261300\tbest: 0.1261300 (73)\ttotal: 27.6s\tremaining: 9.7s\n",
      "74:\tlearn: 0.1250009\ttest: 0.1250629\tbest: 0.1250629 (74)\ttotal: 28s\tremaining: 9.32s\n",
      "75:\tlearn: 0.1239887\ttest: 0.1240498\tbest: 0.1240498 (75)\ttotal: 28.4s\tremaining: 8.95s\n",
      "76:\tlearn: 0.1230627\ttest: 0.1231219\tbest: 0.1231219 (76)\ttotal: 28.7s\tremaining: 8.58s\n",
      "77:\tlearn: 0.1221009\ttest: 0.1221604\tbest: 0.1221604 (77)\ttotal: 29.1s\tremaining: 8.19s\n",
      "78:\tlearn: 0.1212785\ttest: 0.1213353\tbest: 0.1213353 (78)\ttotal: 29.4s\tremaining: 7.82s\n",
      "79:\tlearn: 0.1204735\ttest: 0.1205289\tbest: 0.1205289 (79)\ttotal: 29.8s\tremaining: 7.45s\n",
      "80:\tlearn: 0.1197318\ttest: 0.1197875\tbest: 0.1197875 (80)\ttotal: 30.1s\tremaining: 7.07s\n",
      "81:\tlearn: 0.1189685\ttest: 0.1190217\tbest: 0.1190217 (81)\ttotal: 30.5s\tremaining: 6.69s\n",
      "82:\tlearn: 0.1181768\ttest: 0.1182263\tbest: 0.1182263 (82)\ttotal: 30.8s\tremaining: 6.32s\n",
      "83:\tlearn: 0.1175357\ttest: 0.1175851\tbest: 0.1175851 (83)\ttotal: 31.2s\tremaining: 5.93s\n",
      "84:\tlearn: 0.1168273\ttest: 0.1168729\tbest: 0.1168729 (84)\ttotal: 31.5s\tremaining: 5.57s\n",
      "85:\tlearn: 0.1161855\ttest: 0.1162286\tbest: 0.1162286 (85)\ttotal: 31.9s\tremaining: 5.19s\n",
      "86:\tlearn: 0.1154907\ttest: 0.1155345\tbest: 0.1155345 (86)\ttotal: 32.2s\tremaining: 4.82s\n",
      "87:\tlearn: 0.1148304\ttest: 0.1148718\tbest: 0.1148718 (87)\ttotal: 32.6s\tremaining: 4.45s\n",
      "88:\tlearn: 0.1142903\ttest: 0.1143283\tbest: 0.1143283 (88)\ttotal: 33s\tremaining: 4.08s\n",
      "89:\tlearn: 0.1137469\ttest: 0.1137813\tbest: 0.1137813 (89)\ttotal: 33.3s\tremaining: 3.7s\n",
      "90:\tlearn: 0.1132046\ttest: 0.1132380\tbest: 0.1132380 (90)\ttotal: 33.7s\tremaining: 3.33s\n",
      "91:\tlearn: 0.1127124\ttest: 0.1127446\tbest: 0.1127446 (91)\ttotal: 34s\tremaining: 2.96s\n",
      "92:\tlearn: 0.1121672\ttest: 0.1121988\tbest: 0.1121988 (92)\ttotal: 34.4s\tremaining: 2.58s\n",
      "93:\tlearn: 0.1117384\ttest: 0.1117693\tbest: 0.1117693 (93)\ttotal: 34.7s\tremaining: 2.21s\n",
      "94:\tlearn: 0.1113265\ttest: 0.1113572\tbest: 0.1113572 (94)\ttotal: 35s\tremaining: 1.84s\n",
      "95:\tlearn: 0.1109030\ttest: 0.1109322\tbest: 0.1109322 (95)\ttotal: 35.3s\tremaining: 1.47s\n",
      "96:\tlearn: 0.1104253\ttest: 0.1104550\tbest: 0.1104550 (96)\ttotal: 35.6s\tremaining: 1.1s\n",
      "97:\tlearn: 0.1099709\ttest: 0.1099982\tbest: 0.1099982 (97)\ttotal: 36s\tremaining: 734ms\n",
      "98:\tlearn: 0.1095253\ttest: 0.1095530\tbest: 0.1095530 (98)\ttotal: 36.3s\tremaining: 367ms\n",
      "99:\tlearn: 0.1091712\ttest: 0.1091970\tbest: 0.1091970 (99)\ttotal: 36.6s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.1091969821\n",
      "bestIteration = 99\n",
      "\n",
      "8:\tloss: 0.1091970\tbest: 0.0911950 (7)\ttotal: 4m 38s\tremaining: 3m 36s\n",
      "0:\tlearn: 0.5109279\ttest: 0.5112795\tbest: 0.5112795 (0)\ttotal: 394ms\tremaining: 39s\n",
      "1:\tlearn: 0.4637059\ttest: 0.4640145\tbest: 0.4640145 (1)\ttotal: 752ms\tremaining: 36.9s\n",
      "2:\tlearn: 0.4215289\ttest: 0.4218210\tbest: 0.4218210 (2)\ttotal: 1.1s\tremaining: 35.7s\n",
      "3:\tlearn: 0.3834790\ttest: 0.3837547\tbest: 0.3837547 (3)\ttotal: 1.48s\tremaining: 35.5s\n",
      "4:\tlearn: 0.3498172\ttest: 0.3500708\tbest: 0.3500708 (4)\ttotal: 1.87s\tremaining: 35.5s\n",
      "5:\tlearn: 0.3196398\ttest: 0.3198813\tbest: 0.3198813 (5)\ttotal: 2.24s\tremaining: 35.1s\n",
      "6:\tlearn: 0.2930632\ttest: 0.2932841\tbest: 0.2932841 (6)\ttotal: 2.59s\tremaining: 34.5s\n",
      "7:\tlearn: 0.2692844\ttest: 0.2694838\tbest: 0.2694838 (7)\ttotal: 2.96s\tremaining: 34s\n",
      "8:\tlearn: 0.2482368\ttest: 0.2484094\tbest: 0.2484094 (8)\ttotal: 3.33s\tremaining: 33.6s\n",
      "9:\tlearn: 0.2295297\ttest: 0.2296809\tbest: 0.2296809 (9)\ttotal: 3.69s\tremaining: 33.2s\n",
      "10:\tlearn: 0.2129025\ttest: 0.2130521\tbest: 0.2130521 (10)\ttotal: 4.04s\tremaining: 32.7s\n",
      "11:\tlearn: 0.1984842\ttest: 0.1986222\tbest: 0.1986222 (11)\ttotal: 4.41s\tremaining: 32.3s\n",
      "12:\tlearn: 0.1857084\ttest: 0.1858458\tbest: 0.1858458 (12)\ttotal: 4.83s\tremaining: 32.3s\n",
      "13:\tlearn: 0.1745220\ttest: 0.1746388\tbest: 0.1746388 (13)\ttotal: 5.35s\tremaining: 32.9s\n",
      "14:\tlearn: 0.1648249\ttest: 0.1649245\tbest: 0.1649245 (14)\ttotal: 5.84s\tremaining: 33.1s\n",
      "15:\tlearn: 0.1564276\ttest: 0.1565049\tbest: 0.1565049 (15)\ttotal: 6.36s\tremaining: 33.4s\n",
      "16:\tlearn: 0.1492175\ttest: 0.1492958\tbest: 0.1492958 (16)\ttotal: 6.72s\tremaining: 32.8s\n",
      "17:\tlearn: 0.1426831\ttest: 0.1427676\tbest: 0.1427676 (17)\ttotal: 7.1s\tremaining: 32.3s\n",
      "18:\tlearn: 0.1371912\ttest: 0.1372585\tbest: 0.1372585 (18)\ttotal: 7.45s\tremaining: 31.8s\n",
      "19:\tlearn: 0.1324951\ttest: 0.1325515\tbest: 0.1325515 (19)\ttotal: 7.81s\tremaining: 31.2s\n",
      "20:\tlearn: 0.1284881\ttest: 0.1285373\tbest: 0.1285373 (20)\ttotal: 8.2s\tremaining: 30.9s\n",
      "21:\tlearn: 0.1250803\ttest: 0.1251184\tbest: 0.1251184 (21)\ttotal: 8.57s\tremaining: 30.4s\n",
      "22:\tlearn: 0.1222279\ttest: 0.1222674\tbest: 0.1222674 (22)\ttotal: 8.89s\tremaining: 29.8s\n",
      "23:\tlearn: 0.1193812\ttest: 0.1194208\tbest: 0.1194208 (23)\ttotal: 9.31s\tremaining: 29.5s\n",
      "24:\tlearn: 0.1172449\ttest: 0.1172741\tbest: 0.1172741 (24)\ttotal: 9.67s\tremaining: 29s\n",
      "25:\tlearn: 0.1153045\ttest: 0.1153199\tbest: 0.1153199 (25)\ttotal: 10s\tremaining: 28.5s\n",
      "26:\tlearn: 0.1136258\ttest: 0.1136373\tbest: 0.1136373 (26)\ttotal: 10.4s\tremaining: 28s\n",
      "27:\tlearn: 0.1119265\ttest: 0.1119434\tbest: 0.1119434 (27)\ttotal: 10.7s\tremaining: 27.5s\n",
      "28:\tlearn: 0.1103541\ttest: 0.1103728\tbest: 0.1103728 (28)\ttotal: 11s\tremaining: 27s\n",
      "29:\tlearn: 0.1088722\ttest: 0.1088767\tbest: 0.1088767 (29)\ttotal: 11.4s\tremaining: 26.6s\n",
      "30:\tlearn: 0.1078941\ttest: 0.1078979\tbest: 0.1078979 (30)\ttotal: 11.8s\tremaining: 26.2s\n",
      "31:\tlearn: 0.1067900\ttest: 0.1067877\tbest: 0.1067877 (31)\ttotal: 12.2s\tremaining: 25.8s\n",
      "32:\tlearn: 0.1059028\ttest: 0.1058962\tbest: 0.1058962 (32)\ttotal: 12.5s\tremaining: 25.4s\n",
      "33:\tlearn: 0.1047742\ttest: 0.1047557\tbest: 0.1047557 (33)\ttotal: 12.9s\tremaining: 25s\n",
      "34:\tlearn: 0.1039752\ttest: 0.1039459\tbest: 0.1039459 (34)\ttotal: 13.2s\tremaining: 24.5s\n",
      "35:\tlearn: 0.1031522\ttest: 0.1031188\tbest: 0.1031188 (35)\ttotal: 13.6s\tremaining: 24.2s\n",
      "36:\tlearn: 0.1026012\ttest: 0.1025653\tbest: 0.1025653 (36)\ttotal: 13.9s\tremaining: 23.7s\n",
      "37:\tlearn: 0.1017506\ttest: 0.1017141\tbest: 0.1017141 (37)\ttotal: 14.2s\tremaining: 23.2s\n",
      "38:\tlearn: 0.1011114\ttest: 0.1010744\tbest: 0.1010744 (38)\ttotal: 14.6s\tremaining: 22.8s\n",
      "39:\tlearn: 0.1006871\ttest: 0.1006558\tbest: 0.1006558 (39)\ttotal: 14.9s\tremaining: 22.3s\n",
      "40:\tlearn: 0.1000700\ttest: 0.1000413\tbest: 0.1000413 (40)\ttotal: 15.2s\tremaining: 21.9s\n",
      "41:\tlearn: 0.0996277\ttest: 0.0995929\tbest: 0.0995929 (41)\ttotal: 15.6s\tremaining: 21.5s\n",
      "42:\tlearn: 0.0992064\ttest: 0.0991717\tbest: 0.0991717 (42)\ttotal: 15.9s\tremaining: 21.1s\n",
      "43:\tlearn: 0.0987241\ttest: 0.0986927\tbest: 0.0986927 (43)\ttotal: 16.2s\tremaining: 20.6s\n",
      "44:\tlearn: 0.0983695\ttest: 0.0983354\tbest: 0.0983354 (44)\ttotal: 16.5s\tremaining: 20.2s\n",
      "45:\tlearn: 0.0981003\ttest: 0.0980650\tbest: 0.0980650 (45)\ttotal: 16.8s\tremaining: 19.8s\n",
      "46:\tlearn: 0.0977650\ttest: 0.0977233\tbest: 0.0977233 (46)\ttotal: 17.2s\tremaining: 19.4s\n",
      "47:\tlearn: 0.0974036\ttest: 0.0973642\tbest: 0.0973642 (47)\ttotal: 17.5s\tremaining: 19s\n",
      "48:\tlearn: 0.0971087\ttest: 0.0970724\tbest: 0.0970724 (48)\ttotal: 17.9s\tremaining: 18.6s\n",
      "49:\tlearn: 0.0967468\ttest: 0.0967104\tbest: 0.0967104 (49)\ttotal: 18.2s\tremaining: 18.2s\n",
      "50:\tlearn: 0.0964693\ttest: 0.0964358\tbest: 0.0964358 (50)\ttotal: 18.5s\tremaining: 17.8s\n",
      "51:\tlearn: 0.0962472\ttest: 0.0962111\tbest: 0.0962111 (51)\ttotal: 18.8s\tremaining: 17.4s\n",
      "52:\tlearn: 0.0957698\ttest: 0.0957314\tbest: 0.0957314 (52)\ttotal: 19.2s\tremaining: 17s\n",
      "53:\tlearn: 0.0954788\ttest: 0.0954437\tbest: 0.0954437 (53)\ttotal: 19.5s\tremaining: 16.6s\n",
      "54:\tlearn: 0.0952432\ttest: 0.0952060\tbest: 0.0952060 (54)\ttotal: 19.8s\tremaining: 16.2s\n",
      "55:\tlearn: 0.0949811\ttest: 0.0949437\tbest: 0.0949437 (55)\ttotal: 20.2s\tremaining: 15.9s\n",
      "56:\tlearn: 0.0946489\ttest: 0.0946092\tbest: 0.0946092 (56)\ttotal: 20.5s\tremaining: 15.5s\n",
      "57:\tlearn: 0.0943610\ttest: 0.0943272\tbest: 0.0943272 (57)\ttotal: 20.9s\tremaining: 15.1s\n",
      "58:\tlearn: 0.0941838\ttest: 0.0941486\tbest: 0.0941486 (58)\ttotal: 21.2s\tremaining: 14.7s\n",
      "59:\tlearn: 0.0939432\ttest: 0.0939064\tbest: 0.0939064 (59)\ttotal: 21.5s\tremaining: 14.3s\n",
      "60:\tlearn: 0.0937883\ttest: 0.0937513\tbest: 0.0937513 (60)\ttotal: 21.8s\tremaining: 14s\n",
      "61:\tlearn: 0.0935737\ttest: 0.0935381\tbest: 0.0935381 (61)\ttotal: 22.1s\tremaining: 13.6s\n",
      "62:\tlearn: 0.0934455\ttest: 0.0934080\tbest: 0.0934080 (62)\ttotal: 22.4s\tremaining: 13.2s\n",
      "63:\tlearn: 0.0931649\ttest: 0.0931262\tbest: 0.0931262 (63)\ttotal: 22.8s\tremaining: 12.8s\n",
      "64:\tlearn: 0.0930324\ttest: 0.0929907\tbest: 0.0929907 (64)\ttotal: 23.2s\tremaining: 12.5s\n",
      "65:\tlearn: 0.0928948\ttest: 0.0928561\tbest: 0.0928561 (65)\ttotal: 23.5s\tremaining: 12.1s\n",
      "66:\tlearn: 0.0926630\ttest: 0.0926214\tbest: 0.0926214 (66)\ttotal: 23.8s\tremaining: 11.7s\n",
      "67:\tlearn: 0.0923134\ttest: 0.0922721\tbest: 0.0922721 (67)\ttotal: 24.2s\tremaining: 11.4s\n",
      "68:\tlearn: 0.0920376\ttest: 0.0920000\tbest: 0.0920000 (68)\ttotal: 24.6s\tremaining: 11s\n",
      "69:\tlearn: 0.0919258\ttest: 0.0918886\tbest: 0.0918886 (69)\ttotal: 24.9s\tremaining: 10.7s\n",
      "70:\tlearn: 0.0918027\ttest: 0.0917649\tbest: 0.0917649 (70)\ttotal: 25.2s\tremaining: 10.3s\n",
      "71:\tlearn: 0.0917283\ttest: 0.0916924\tbest: 0.0916924 (71)\ttotal: 25.4s\tremaining: 9.89s\n",
      "72:\tlearn: 0.0916590\ttest: 0.0916239\tbest: 0.0916239 (72)\ttotal: 25.7s\tremaining: 9.51s\n",
      "73:\tlearn: 0.0915285\ttest: 0.0914936\tbest: 0.0914936 (73)\ttotal: 26s\tremaining: 9.13s\n",
      "74:\tlearn: 0.0913314\ttest: 0.0912956\tbest: 0.0912956 (74)\ttotal: 26.3s\tremaining: 8.75s\n",
      "75:\tlearn: 0.0912206\ttest: 0.0911845\tbest: 0.0911845 (75)\ttotal: 26.6s\tremaining: 8.39s\n",
      "76:\tlearn: 0.0909848\ttest: 0.0909512\tbest: 0.0909512 (76)\ttotal: 26.9s\tremaining: 8.04s\n",
      "77:\tlearn: 0.0908412\ttest: 0.0908068\tbest: 0.0908068 (77)\ttotal: 27.3s\tremaining: 7.69s\n",
      "78:\tlearn: 0.0907450\ttest: 0.0907128\tbest: 0.0907128 (78)\ttotal: 27.5s\tremaining: 7.32s\n",
      "79:\tlearn: 0.0905574\ttest: 0.0905247\tbest: 0.0905247 (79)\ttotal: 27.9s\tremaining: 6.99s\n",
      "80:\tlearn: 0.0903544\ttest: 0.0903220\tbest: 0.0903220 (80)\ttotal: 28.2s\tremaining: 6.63s\n",
      "81:\tlearn: 0.0901872\ttest: 0.0901579\tbest: 0.0901579 (81)\ttotal: 28.6s\tremaining: 6.29s\n",
      "82:\tlearn: 0.0900583\ttest: 0.0900295\tbest: 0.0900295 (82)\ttotal: 28.9s\tremaining: 5.92s\n",
      "83:\tlearn: 0.0899783\ttest: 0.0899489\tbest: 0.0899489 (83)\ttotal: 29.2s\tremaining: 5.57s\n",
      "84:\tlearn: 0.0898398\ttest: 0.0898089\tbest: 0.0898089 (84)\ttotal: 29.5s\tremaining: 5.21s\n",
      "85:\tlearn: 0.0896888\ttest: 0.0896580\tbest: 0.0896580 (85)\ttotal: 29.9s\tremaining: 4.86s\n",
      "86:\tlearn: 0.0895850\ttest: 0.0895540\tbest: 0.0895540 (86)\ttotal: 30.1s\tremaining: 4.5s\n",
      "87:\tlearn: 0.0894468\ttest: 0.0894202\tbest: 0.0894202 (87)\ttotal: 30.5s\tremaining: 4.16s\n",
      "88:\tlearn: 0.0892575\ttest: 0.0892366\tbest: 0.0892366 (88)\ttotal: 30.8s\tremaining: 3.81s\n",
      "89:\tlearn: 0.0891244\ttest: 0.0891037\tbest: 0.0891037 (89)\ttotal: 31.2s\tremaining: 3.47s\n",
      "90:\tlearn: 0.0890305\ttest: 0.0890062\tbest: 0.0890062 (90)\ttotal: 31.5s\tremaining: 3.11s\n",
      "91:\tlearn: 0.0889194\ttest: 0.0888970\tbest: 0.0888970 (91)\ttotal: 31.9s\tremaining: 2.77s\n",
      "92:\tlearn: 0.0887018\ttest: 0.0886810\tbest: 0.0886810 (92)\ttotal: 32.2s\tremaining: 2.42s\n",
      "93:\tlearn: 0.0885722\ttest: 0.0885515\tbest: 0.0885515 (93)\ttotal: 32.6s\tremaining: 2.08s\n",
      "94:\tlearn: 0.0884673\ttest: 0.0884454\tbest: 0.0884454 (94)\ttotal: 32.9s\tremaining: 1.73s\n",
      "95:\tlearn: 0.0883592\ttest: 0.0883441\tbest: 0.0883441 (95)\ttotal: 33.3s\tremaining: 1.39s\n",
      "96:\tlearn: 0.0882855\ttest: 0.0882704\tbest: 0.0882704 (96)\ttotal: 33.5s\tremaining: 1.04s\n",
      "97:\tlearn: 0.0882209\ttest: 0.0882052\tbest: 0.0882052 (97)\ttotal: 33.8s\tremaining: 691ms\n",
      "98:\tlearn: 0.0880626\ttest: 0.0880520\tbest: 0.0880520 (98)\ttotal: 34.2s\tremaining: 345ms\n",
      "99:\tlearn: 0.0879453\ttest: 0.0879360\tbest: 0.0879360 (99)\ttotal: 34.5s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.08793595582\n",
      "bestIteration = 99\n",
      "\n",
      "9:\tloss: 0.0879360\tbest: 0.0879360 (9)\ttotal: 5m 13s\tremaining: 3m 8s\n",
      "0:\tlearn: 0.5477899\ttest: 0.5481642\tbest: 0.5481642 (0)\ttotal: 395ms\tremaining: 58.8s\n",
      "1:\tlearn: 0.5324462\ttest: 0.5328088\tbest: 0.5328088 (1)\ttotal: 764ms\tremaining: 56.5s\n",
      "2:\tlearn: 0.5175868\ttest: 0.5179442\tbest: 0.5179442 (2)\ttotal: 1.13s\tremaining: 55.3s\n",
      "3:\tlearn: 0.5032060\ttest: 0.5035516\tbest: 0.5035516 (3)\ttotal: 1.49s\tremaining: 54.5s\n",
      "4:\tlearn: 0.4892458\ttest: 0.4895833\tbest: 0.4895833 (4)\ttotal: 1.86s\tremaining: 53.9s\n",
      "5:\tlearn: 0.4757004\ttest: 0.4760336\tbest: 0.4760336 (5)\ttotal: 2.31s\tremaining: 55.4s\n",
      "6:\tlearn: 0.4625569\ttest: 0.4628842\tbest: 0.4628842 (6)\ttotal: 2.81s\tremaining: 57.5s\n",
      "7:\tlearn: 0.4498186\ttest: 0.4501373\tbest: 0.4501373 (7)\ttotal: 3.31s\tremaining: 58.9s\n",
      "8:\tlearn: 0.4375075\ttest: 0.4378156\tbest: 0.4378156 (8)\ttotal: 3.8s\tremaining: 59.6s\n",
      "9:\tlearn: 0.4255753\ttest: 0.4258785\tbest: 0.4258785 (9)\ttotal: 4.18s\tremaining: 58.5s\n",
      "10:\tlearn: 0.4140739\ttest: 0.4143693\tbest: 0.4143693 (10)\ttotal: 4.55s\tremaining: 57.5s\n",
      "11:\tlearn: 0.4029526\ttest: 0.4032388\tbest: 0.4032388 (11)\ttotal: 4.91s\tremaining: 56.5s\n",
      "12:\tlearn: 0.3921416\ttest: 0.3924243\tbest: 0.3924243 (12)\ttotal: 5.28s\tremaining: 55.6s\n",
      "13:\tlearn: 0.3816650\ttest: 0.3819388\tbest: 0.3819388 (13)\ttotal: 5.64s\tremaining: 54.8s\n",
      "14:\tlearn: 0.3715609\ttest: 0.3718276\tbest: 0.3718276 (14)\ttotal: 6s\tremaining: 54s\n",
      "15:\tlearn: 0.3617383\ttest: 0.3620031\tbest: 0.3620031 (15)\ttotal: 6.38s\tremaining: 53.4s\n",
      "16:\tlearn: 0.3522771\ttest: 0.3525341\tbest: 0.3525341 (16)\ttotal: 6.76s\tremaining: 52.9s\n",
      "17:\tlearn: 0.3430697\ttest: 0.3433232\tbest: 0.3433232 (17)\ttotal: 7.17s\tremaining: 52.6s\n",
      "18:\tlearn: 0.3342020\ttest: 0.3344522\tbest: 0.3344522 (18)\ttotal: 7.53s\tremaining: 51.9s\n",
      "19:\tlearn: 0.3256181\ttest: 0.3258658\tbest: 0.3258658 (19)\ttotal: 7.89s\tremaining: 51.3s\n",
      "20:\tlearn: 0.3173265\ttest: 0.3175752\tbest: 0.3175752 (20)\ttotal: 8.26s\tremaining: 50.7s\n",
      "21:\tlearn: 0.3092722\ttest: 0.3095186\tbest: 0.3095186 (21)\ttotal: 8.64s\tremaining: 50.3s\n",
      "22:\tlearn: 0.3014931\ttest: 0.3017315\tbest: 0.3017315 (22)\ttotal: 9.02s\tremaining: 49.8s\n",
      "23:\tlearn: 0.2939661\ttest: 0.2942016\tbest: 0.2942016 (23)\ttotal: 9.39s\tremaining: 49.3s\n",
      "24:\tlearn: 0.2867152\ttest: 0.2869437\tbest: 0.2869437 (24)\ttotal: 9.75s\tremaining: 48.8s\n",
      "25:\tlearn: 0.2797200\ttest: 0.2799455\tbest: 0.2799455 (25)\ttotal: 10.1s\tremaining: 48.2s\n",
      "26:\tlearn: 0.2729910\ttest: 0.2732098\tbest: 0.2732098 (26)\ttotal: 10.5s\tremaining: 47.7s\n",
      "27:\tlearn: 0.2665095\ttest: 0.2667227\tbest: 0.2667227 (27)\ttotal: 10.8s\tremaining: 47.3s\n",
      "28:\tlearn: 0.2602353\ttest: 0.2604481\tbest: 0.2604481 (28)\ttotal: 11.2s\tremaining: 46.7s\n",
      "29:\tlearn: 0.2541400\ttest: 0.2543457\tbest: 0.2543457 (29)\ttotal: 11.6s\tremaining: 46.3s\n",
      "30:\tlearn: 0.2482918\ttest: 0.2484948\tbest: 0.2484948 (30)\ttotal: 11.9s\tremaining: 45.7s\n",
      "31:\tlearn: 0.2426497\ttest: 0.2428479\tbest: 0.2428479 (31)\ttotal: 12.3s\tremaining: 45.3s\n",
      "32:\tlearn: 0.2371625\ttest: 0.2373541\tbest: 0.2373541 (32)\ttotal: 12.7s\tremaining: 44.9s\n",
      "33:\tlearn: 0.2318764\ttest: 0.2320608\tbest: 0.2320608 (33)\ttotal: 13.1s\tremaining: 44.5s\n",
      "34:\tlearn: 0.2267976\ttest: 0.2269759\tbest: 0.2269759 (34)\ttotal: 13.4s\tremaining: 44.1s\n",
      "35:\tlearn: 0.2218714\ttest: 0.2220506\tbest: 0.2220506 (35)\ttotal: 13.8s\tremaining: 43.8s\n",
      "36:\tlearn: 0.2171223\ttest: 0.2172962\tbest: 0.2172962 (36)\ttotal: 14.2s\tremaining: 43.3s\n",
      "37:\tlearn: 0.2125409\ttest: 0.2127131\tbest: 0.2127131 (37)\ttotal: 14.5s\tremaining: 42.9s\n",
      "38:\tlearn: 0.2081376\ttest: 0.2083051\tbest: 0.2083051 (38)\ttotal: 14.9s\tremaining: 42.4s\n",
      "39:\tlearn: 0.2039086\ttest: 0.2040687\tbest: 0.2040687 (39)\ttotal: 15.3s\tremaining: 42s\n",
      "40:\tlearn: 0.1998037\ttest: 0.1999581\tbest: 0.1999581 (40)\ttotal: 15.6s\tremaining: 41.6s\n",
      "41:\tlearn: 0.1958906\ttest: 0.1960436\tbest: 0.1960436 (41)\ttotal: 16s\tremaining: 41.1s\n",
      "42:\tlearn: 0.1921140\ttest: 0.1922613\tbest: 0.1922613 (42)\ttotal: 16.4s\tremaining: 40.8s\n",
      "43:\tlearn: 0.1884418\ttest: 0.1885905\tbest: 0.1885905 (43)\ttotal: 16.7s\tremaining: 40.3s\n",
      "44:\tlearn: 0.1849495\ttest: 0.1850925\tbest: 0.1850925 (44)\ttotal: 17.1s\tremaining: 40s\n",
      "45:\tlearn: 0.1815623\ttest: 0.1817000\tbest: 0.1817000 (45)\ttotal: 17.5s\tremaining: 39.6s\n",
      "46:\tlearn: 0.1782995\ttest: 0.1784355\tbest: 0.1784355 (46)\ttotal: 17.8s\tremaining: 39.1s\n",
      "47:\tlearn: 0.1751966\ttest: 0.1753286\tbest: 0.1753286 (47)\ttotal: 18.2s\tremaining: 38.7s\n",
      "48:\tlearn: 0.1722258\ttest: 0.1723537\tbest: 0.1723537 (48)\ttotal: 18.6s\tremaining: 38.2s\n",
      "49:\tlearn: 0.1692636\ttest: 0.1693921\tbest: 0.1693921 (49)\ttotal: 19s\tremaining: 38s\n",
      "50:\tlearn: 0.1664554\ttest: 0.1665820\tbest: 0.1665820 (50)\ttotal: 19.4s\tremaining: 37.6s\n",
      "51:\tlearn: 0.1638216\ttest: 0.1639442\tbest: 0.1639442 (51)\ttotal: 19.7s\tremaining: 37.2s\n",
      "52:\tlearn: 0.1612496\ttest: 0.1613668\tbest: 0.1613668 (52)\ttotal: 20.1s\tremaining: 36.8s\n",
      "53:\tlearn: 0.1587804\ttest: 0.1588977\tbest: 0.1588977 (53)\ttotal: 20.5s\tremaining: 36.4s\n",
      "54:\tlearn: 0.1563738\ttest: 0.1564892\tbest: 0.1564892 (54)\ttotal: 20.9s\tremaining: 36s\n",
      "55:\tlearn: 0.1541158\ttest: 0.1542263\tbest: 0.1542263 (55)\ttotal: 21.2s\tremaining: 35.6s\n",
      "56:\tlearn: 0.1519308\ttest: 0.1520387\tbest: 0.1520387 (56)\ttotal: 21.6s\tremaining: 35.2s\n",
      "57:\tlearn: 0.1498505\ttest: 0.1499553\tbest: 0.1499553 (57)\ttotal: 21.9s\tremaining: 34.7s\n",
      "58:\tlearn: 0.1478581\ttest: 0.1479568\tbest: 0.1479568 (58)\ttotal: 22.3s\tremaining: 34.3s\n",
      "59:\tlearn: 0.1458972\ttest: 0.1459935\tbest: 0.1459935 (59)\ttotal: 22.6s\tremaining: 33.9s\n",
      "60:\tlearn: 0.1440727\ttest: 0.1441651\tbest: 0.1441651 (60)\ttotal: 23s\tremaining: 33.5s\n",
      "61:\tlearn: 0.1423098\ttest: 0.1423983\tbest: 0.1423983 (61)\ttotal: 23.4s\tremaining: 33.2s\n",
      "62:\tlearn: 0.1406208\ttest: 0.1407033\tbest: 0.1407033 (62)\ttotal: 23.8s\tremaining: 32.8s\n",
      "63:\tlearn: 0.1389612\ttest: 0.1390444\tbest: 0.1390444 (63)\ttotal: 24.1s\tremaining: 32.4s\n",
      "64:\tlearn: 0.1374102\ttest: 0.1374933\tbest: 0.1374933 (64)\ttotal: 24.5s\tremaining: 32s\n",
      "65:\tlearn: 0.1359361\ttest: 0.1360143\tbest: 0.1360143 (65)\ttotal: 24.9s\tremaining: 31.6s\n",
      "66:\tlearn: 0.1344344\ttest: 0.1345129\tbest: 0.1345129 (66)\ttotal: 25.2s\tremaining: 31.2s\n",
      "67:\tlearn: 0.1331165\ttest: 0.1331905\tbest: 0.1331905 (67)\ttotal: 25.6s\tremaining: 30.8s\n",
      "68:\tlearn: 0.1318228\ttest: 0.1318935\tbest: 0.1318935 (68)\ttotal: 25.9s\tremaining: 30.4s\n",
      "69:\tlearn: 0.1306020\ttest: 0.1306711\tbest: 0.1306711 (69)\ttotal: 26.3s\tremaining: 30.1s\n",
      "70:\tlearn: 0.1294182\ttest: 0.1294843\tbest: 0.1294843 (70)\ttotal: 26.7s\tremaining: 29.7s\n",
      "71:\tlearn: 0.1282928\ttest: 0.1283551\tbest: 0.1283551 (71)\ttotal: 27.1s\tremaining: 29.4s\n",
      "72:\tlearn: 0.1270933\ttest: 0.1271583\tbest: 0.1271583 (72)\ttotal: 27.5s\tremaining: 29s\n",
      "73:\tlearn: 0.1260687\ttest: 0.1261300\tbest: 0.1261300 (73)\ttotal: 27.9s\tremaining: 28.6s\n",
      "74:\tlearn: 0.1250009\ttest: 0.1250629\tbest: 0.1250629 (74)\ttotal: 28.2s\tremaining: 28.2s\n",
      "75:\tlearn: 0.1239887\ttest: 0.1240498\tbest: 0.1240498 (75)\ttotal: 28.6s\tremaining: 27.8s\n",
      "76:\tlearn: 0.1230627\ttest: 0.1231219\tbest: 0.1231219 (76)\ttotal: 28.9s\tremaining: 27.4s\n",
      "77:\tlearn: 0.1221009\ttest: 0.1221604\tbest: 0.1221604 (77)\ttotal: 29.3s\tremaining: 27s\n",
      "78:\tlearn: 0.1212785\ttest: 0.1213353\tbest: 0.1213353 (78)\ttotal: 29.6s\tremaining: 26.6s\n",
      "79:\tlearn: 0.1204735\ttest: 0.1205289\tbest: 0.1205289 (79)\ttotal: 30s\tremaining: 26.2s\n",
      "80:\tlearn: 0.1197318\ttest: 0.1197875\tbest: 0.1197875 (80)\ttotal: 30.3s\tremaining: 25.8s\n",
      "81:\tlearn: 0.1189685\ttest: 0.1190217\tbest: 0.1190217 (81)\ttotal: 30.7s\tremaining: 25.4s\n",
      "82:\tlearn: 0.1181768\ttest: 0.1182263\tbest: 0.1182263 (82)\ttotal: 31s\tremaining: 25s\n",
      "83:\tlearn: 0.1175357\ttest: 0.1175851\tbest: 0.1175851 (83)\ttotal: 31.3s\tremaining: 24.6s\n",
      "84:\tlearn: 0.1168273\ttest: 0.1168729\tbest: 0.1168729 (84)\ttotal: 31.7s\tremaining: 24.3s\n",
      "85:\tlearn: 0.1161855\ttest: 0.1162286\tbest: 0.1162286 (85)\ttotal: 32.1s\tremaining: 23.9s\n",
      "86:\tlearn: 0.1154907\ttest: 0.1155345\tbest: 0.1155345 (86)\ttotal: 32.4s\tremaining: 23.5s\n",
      "87:\tlearn: 0.1148304\ttest: 0.1148718\tbest: 0.1148718 (87)\ttotal: 32.8s\tremaining: 23.1s\n",
      "88:\tlearn: 0.1142903\ttest: 0.1143283\tbest: 0.1143283 (88)\ttotal: 33.2s\tremaining: 22.7s\n",
      "89:\tlearn: 0.1137469\ttest: 0.1137813\tbest: 0.1137813 (89)\ttotal: 33.5s\tremaining: 22.3s\n",
      "90:\tlearn: 0.1132046\ttest: 0.1132380\tbest: 0.1132380 (90)\ttotal: 33.9s\tremaining: 22s\n",
      "91:\tlearn: 0.1127124\ttest: 0.1127446\tbest: 0.1127446 (91)\ttotal: 34.2s\tremaining: 21.5s\n",
      "92:\tlearn: 0.1121672\ttest: 0.1121988\tbest: 0.1121988 (92)\ttotal: 34.7s\tremaining: 21.3s\n",
      "93:\tlearn: 0.1117384\ttest: 0.1117693\tbest: 0.1117693 (93)\ttotal: 35.1s\tremaining: 20.9s\n",
      "94:\tlearn: 0.1113265\ttest: 0.1113572\tbest: 0.1113572 (94)\ttotal: 35.5s\tremaining: 20.6s\n",
      "95:\tlearn: 0.1109030\ttest: 0.1109322\tbest: 0.1109322 (95)\ttotal: 36s\tremaining: 20.2s\n",
      "96:\tlearn: 0.1104253\ttest: 0.1104550\tbest: 0.1104550 (96)\ttotal: 36.3s\tremaining: 19.8s\n",
      "97:\tlearn: 0.1099709\ttest: 0.1099982\tbest: 0.1099982 (97)\ttotal: 36.6s\tremaining: 19.4s\n",
      "98:\tlearn: 0.1095253\ttest: 0.1095530\tbest: 0.1095530 (98)\ttotal: 37.1s\tremaining: 19.1s\n",
      "99:\tlearn: 0.1091712\ttest: 0.1091970\tbest: 0.1091970 (99)\ttotal: 37.4s\tremaining: 18.7s\n",
      "100:\tlearn: 0.1087696\ttest: 0.1087925\tbest: 0.1087925 (100)\ttotal: 37.8s\tremaining: 18.3s\n",
      "101:\tlearn: 0.1084355\ttest: 0.1084557\tbest: 0.1084557 (101)\ttotal: 38.1s\tremaining: 17.9s\n",
      "102:\tlearn: 0.1081285\ttest: 0.1081466\tbest: 0.1081466 (102)\ttotal: 38.5s\tremaining: 17.6s\n",
      "103:\tlearn: 0.1077430\ttest: 0.1077610\tbest: 0.1077610 (103)\ttotal: 38.8s\tremaining: 17.2s\n",
      "104:\tlearn: 0.1074397\ttest: 0.1074547\tbest: 0.1074547 (104)\ttotal: 39.1s\tremaining: 16.8s\n",
      "105:\tlearn: 0.1071223\ttest: 0.1071341\tbest: 0.1071341 (105)\ttotal: 39.5s\tremaining: 16.4s\n",
      "106:\tlearn: 0.1068624\ttest: 0.1068753\tbest: 0.1068753 (106)\ttotal: 39.8s\tremaining: 16s\n",
      "107:\tlearn: 0.1065491\ttest: 0.1065630\tbest: 0.1065630 (107)\ttotal: 40.1s\tremaining: 15.6s\n",
      "108:\tlearn: 0.1062219\ttest: 0.1062392\tbest: 0.1062392 (108)\ttotal: 40.5s\tremaining: 15.2s\n",
      "109:\tlearn: 0.1059233\ttest: 0.1059410\tbest: 0.1059410 (109)\ttotal: 40.9s\tremaining: 14.9s\n",
      "110:\tlearn: 0.1056858\ttest: 0.1057027\tbest: 0.1057027 (110)\ttotal: 41.1s\tremaining: 14.5s\n",
      "111:\tlearn: 0.1054422\ttest: 0.1054580\tbest: 0.1054580 (111)\ttotal: 41.5s\tremaining: 14.1s\n",
      "112:\tlearn: 0.1051727\ttest: 0.1051870\tbest: 0.1051870 (112)\ttotal: 41.9s\tremaining: 13.7s\n",
      "113:\tlearn: 0.1049250\ttest: 0.1049380\tbest: 0.1049380 (113)\ttotal: 42.2s\tremaining: 13.3s\n",
      "114:\tlearn: 0.1047252\ttest: 0.1047374\tbest: 0.1047374 (114)\ttotal: 42.6s\tremaining: 12.9s\n",
      "115:\tlearn: 0.1045222\ttest: 0.1045326\tbest: 0.1045326 (115)\ttotal: 42.9s\tremaining: 12.6s\n",
      "116:\tlearn: 0.1041946\ttest: 0.1042039\tbest: 0.1042039 (116)\ttotal: 43.2s\tremaining: 12.2s\n",
      "117:\tlearn: 0.1039425\ttest: 0.1039525\tbest: 0.1039525 (117)\ttotal: 43.6s\tremaining: 11.8s\n",
      "118:\tlearn: 0.1037073\ttest: 0.1037169\tbest: 0.1037169 (118)\ttotal: 43.9s\tremaining: 11.4s\n",
      "119:\tlearn: 0.1034041\ttest: 0.1034122\tbest: 0.1034122 (119)\ttotal: 44.3s\tremaining: 11.1s\n",
      "120:\tlearn: 0.1031283\ttest: 0.1031350\tbest: 0.1031350 (120)\ttotal: 44.6s\tremaining: 10.7s\n",
      "121:\tlearn: 0.1029319\ttest: 0.1029371\tbest: 0.1029371 (121)\ttotal: 45s\tremaining: 10.3s\n",
      "122:\tlearn: 0.1027422\ttest: 0.1027482\tbest: 0.1027482 (122)\ttotal: 45.3s\tremaining: 9.93s\n",
      "123:\tlearn: 0.1025953\ttest: 0.1026026\tbest: 0.1026026 (123)\ttotal: 45.5s\tremaining: 9.55s\n",
      "124:\tlearn: 0.1024030\ttest: 0.1024075\tbest: 0.1024075 (124)\ttotal: 45.9s\tremaining: 9.18s\n",
      "125:\tlearn: 0.1022597\ttest: 0.1022632\tbest: 0.1022632 (125)\ttotal: 46.2s\tremaining: 8.8s\n",
      "126:\tlearn: 0.1021040\ttest: 0.1021064\tbest: 0.1021064 (126)\ttotal: 46.5s\tremaining: 8.43s\n",
      "127:\tlearn: 0.1018188\ttest: 0.1018183\tbest: 0.1018183 (127)\ttotal: 47s\tremaining: 8.07s\n",
      "128:\tlearn: 0.1016167\ttest: 0.1016163\tbest: 0.1016163 (128)\ttotal: 47.3s\tremaining: 7.71s\n",
      "129:\tlearn: 0.1014584\ttest: 0.1014565\tbest: 0.1014565 (129)\ttotal: 47.7s\tremaining: 7.33s\n",
      "130:\tlearn: 0.1013072\ttest: 0.1013035\tbest: 0.1013035 (130)\ttotal: 48s\tremaining: 6.96s\n",
      "131:\tlearn: 0.1011600\ttest: 0.1011554\tbest: 0.1011554 (131)\ttotal: 48.3s\tremaining: 6.59s\n",
      "132:\tlearn: 0.1009207\ttest: 0.1009148\tbest: 0.1009148 (132)\ttotal: 48.7s\tremaining: 6.22s\n",
      "133:\tlearn: 0.1007447\ttest: 0.1007382\tbest: 0.1007382 (133)\ttotal: 49s\tremaining: 5.85s\n",
      "134:\tlearn: 0.1006145\ttest: 0.1006065\tbest: 0.1006065 (134)\ttotal: 49.4s\tremaining: 5.48s\n",
      "135:\tlearn: 0.1004555\ttest: 0.1004479\tbest: 0.1004479 (135)\ttotal: 49.7s\tremaining: 5.12s\n",
      "136:\tlearn: 0.1003136\ttest: 0.1003059\tbest: 0.1003059 (136)\ttotal: 50s\tremaining: 4.75s\n",
      "137:\tlearn: 0.1000713\ttest: 0.1000602\tbest: 0.1000602 (137)\ttotal: 50.4s\tremaining: 4.38s\n",
      "138:\tlearn: 0.0999305\ttest: 0.0999199\tbest: 0.0999199 (138)\ttotal: 50.7s\tremaining: 4.01s\n",
      "139:\tlearn: 0.0997863\ttest: 0.0997770\tbest: 0.0997770 (139)\ttotal: 51s\tremaining: 3.65s\n",
      "140:\tlearn: 0.0996260\ttest: 0.0996170\tbest: 0.0996170 (140)\ttotal: 51.4s\tremaining: 3.28s\n",
      "141:\tlearn: 0.0994986\ttest: 0.0994891\tbest: 0.0994891 (141)\ttotal: 51.7s\tremaining: 2.91s\n",
      "142:\tlearn: 0.0993544\ttest: 0.0993451\tbest: 0.0993451 (142)\ttotal: 52.1s\tremaining: 2.55s\n",
      "143:\tlearn: 0.0992693\ttest: 0.0992603\tbest: 0.0992603 (143)\ttotal: 52.4s\tremaining: 2.18s\n",
      "144:\tlearn: 0.0991782\ttest: 0.0991694\tbest: 0.0991694 (144)\ttotal: 52.6s\tremaining: 1.81s\n",
      "145:\tlearn: 0.0990844\ttest: 0.0990747\tbest: 0.0990747 (145)\ttotal: 52.9s\tremaining: 1.45s\n",
      "146:\tlearn: 0.0988812\ttest: 0.0988696\tbest: 0.0988696 (146)\ttotal: 53.3s\tremaining: 1.09s\n",
      "147:\tlearn: 0.0987472\ttest: 0.0987353\tbest: 0.0987353 (147)\ttotal: 53.6s\tremaining: 725ms\n",
      "148:\tlearn: 0.0986168\ttest: 0.0986049\tbest: 0.0986049 (148)\ttotal: 54s\tremaining: 362ms\n",
      "149:\tlearn: 0.0984592\ttest: 0.0984479\tbest: 0.0984479 (149)\ttotal: 54.4s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.09844785215\n",
      "bestIteration = 149\n",
      "\n",
      "10:\tloss: 0.0984479\tbest: 0.0879360 (9)\ttotal: 6m 8s\tremaining: 2m 47s\n",
      "0:\tlearn: 0.5109279\ttest: 0.5112795\tbest: 0.5112795 (0)\ttotal: 403ms\tremaining: 60s\n",
      "1:\tlearn: 0.4637059\ttest: 0.4640145\tbest: 0.4640145 (1)\ttotal: 768ms\tremaining: 56.8s\n",
      "2:\tlearn: 0.4215289\ttest: 0.4218210\tbest: 0.4218210 (2)\ttotal: 1.12s\tremaining: 55.1s\n",
      "3:\tlearn: 0.3834790\ttest: 0.3837547\tbest: 0.3837547 (3)\ttotal: 1.49s\tremaining: 54.5s\n",
      "4:\tlearn: 0.3498172\ttest: 0.3500708\tbest: 0.3500708 (4)\ttotal: 1.85s\tremaining: 53.7s\n",
      "5:\tlearn: 0.3196398\ttest: 0.3198813\tbest: 0.3198813 (5)\ttotal: 2.25s\tremaining: 54.1s\n",
      "6:\tlearn: 0.2930632\ttest: 0.2932841\tbest: 0.2932841 (6)\ttotal: 2.62s\tremaining: 53.6s\n",
      "7:\tlearn: 0.2692844\ttest: 0.2694838\tbest: 0.2694838 (7)\ttotal: 3s\tremaining: 53.2s\n",
      "8:\tlearn: 0.2482368\ttest: 0.2484094\tbest: 0.2484094 (8)\ttotal: 3.37s\tremaining: 52.7s\n",
      "9:\tlearn: 0.2295297\ttest: 0.2296809\tbest: 0.2296809 (9)\ttotal: 3.73s\tremaining: 52.3s\n",
      "10:\tlearn: 0.2129025\ttest: 0.2130521\tbest: 0.2130521 (10)\ttotal: 4.08s\tremaining: 51.6s\n",
      "11:\tlearn: 0.1984842\ttest: 0.1986222\tbest: 0.1986222 (11)\ttotal: 4.45s\tremaining: 51.2s\n",
      "12:\tlearn: 0.1857084\ttest: 0.1858458\tbest: 0.1858458 (12)\ttotal: 4.83s\tremaining: 50.9s\n",
      "13:\tlearn: 0.1745220\ttest: 0.1746388\tbest: 0.1746388 (13)\ttotal: 5.19s\tremaining: 50.5s\n",
      "14:\tlearn: 0.1648249\ttest: 0.1649245\tbest: 0.1649245 (14)\ttotal: 5.56s\tremaining: 50s\n",
      "15:\tlearn: 0.1564276\ttest: 0.1565049\tbest: 0.1565049 (15)\ttotal: 5.95s\tremaining: 49.8s\n",
      "16:\tlearn: 0.1492175\ttest: 0.1492958\tbest: 0.1492958 (16)\ttotal: 6.29s\tremaining: 49.2s\n",
      "17:\tlearn: 0.1426831\ttest: 0.1427676\tbest: 0.1427676 (17)\ttotal: 6.66s\tremaining: 48.9s\n",
      "18:\tlearn: 0.1371912\ttest: 0.1372585\tbest: 0.1372585 (18)\ttotal: 7.02s\tremaining: 48.4s\n",
      "19:\tlearn: 0.1324951\ttest: 0.1325515\tbest: 0.1325515 (19)\ttotal: 7.39s\tremaining: 48.1s\n",
      "20:\tlearn: 0.1284881\ttest: 0.1285373\tbest: 0.1285373 (20)\ttotal: 7.78s\tremaining: 47.8s\n",
      "21:\tlearn: 0.1250803\ttest: 0.1251184\tbest: 0.1251184 (21)\ttotal: 8.15s\tremaining: 47.4s\n",
      "22:\tlearn: 0.1222279\ttest: 0.1222674\tbest: 0.1222674 (22)\ttotal: 8.47s\tremaining: 46.8s\n",
      "23:\tlearn: 0.1193812\ttest: 0.1194208\tbest: 0.1194208 (23)\ttotal: 8.88s\tremaining: 46.6s\n",
      "24:\tlearn: 0.1172449\ttest: 0.1172741\tbest: 0.1172741 (24)\ttotal: 9.25s\tremaining: 46.3s\n",
      "25:\tlearn: 0.1153045\ttest: 0.1153199\tbest: 0.1153199 (25)\ttotal: 9.61s\tremaining: 45.8s\n",
      "26:\tlearn: 0.1136258\ttest: 0.1136373\tbest: 0.1136373 (26)\ttotal: 9.93s\tremaining: 45.3s\n",
      "27:\tlearn: 0.1119265\ttest: 0.1119434\tbest: 0.1119434 (27)\ttotal: 10.3s\tremaining: 44.8s\n",
      "28:\tlearn: 0.1103541\ttest: 0.1103728\tbest: 0.1103728 (28)\ttotal: 10.6s\tremaining: 44.3s\n",
      "29:\tlearn: 0.1088722\ttest: 0.1088767\tbest: 0.1088767 (29)\ttotal: 11s\tremaining: 44s\n",
      "30:\tlearn: 0.1078941\ttest: 0.1078979\tbest: 0.1078979 (30)\ttotal: 11.3s\tremaining: 43.5s\n",
      "31:\tlearn: 0.1067900\ttest: 0.1067877\tbest: 0.1067877 (31)\ttotal: 11.7s\tremaining: 43.2s\n",
      "32:\tlearn: 0.1059028\ttest: 0.1058962\tbest: 0.1058962 (32)\ttotal: 12.2s\tremaining: 43.1s\n",
      "33:\tlearn: 0.1047742\ttest: 0.1047557\tbest: 0.1047557 (33)\ttotal: 12.7s\tremaining: 43.4s\n",
      "34:\tlearn: 0.1039752\ttest: 0.1039459\tbest: 0.1039459 (34)\ttotal: 13.2s\tremaining: 43.3s\n",
      "35:\tlearn: 0.1031522\ttest: 0.1031188\tbest: 0.1031188 (35)\ttotal: 13.7s\tremaining: 43.3s\n",
      "36:\tlearn: 0.1026012\ttest: 0.1025653\tbest: 0.1025653 (36)\ttotal: 14s\tremaining: 42.7s\n",
      "37:\tlearn: 0.1017506\ttest: 0.1017141\tbest: 0.1017141 (37)\ttotal: 14.3s\tremaining: 42.2s\n",
      "38:\tlearn: 0.1011114\ttest: 0.1010744\tbest: 0.1010744 (38)\ttotal: 14.7s\tremaining: 41.7s\n",
      "39:\tlearn: 0.1006871\ttest: 0.1006558\tbest: 0.1006558 (39)\ttotal: 14.9s\tremaining: 41.1s\n",
      "40:\tlearn: 0.1000700\ttest: 0.1000413\tbest: 0.1000413 (40)\ttotal: 15.3s\tremaining: 40.7s\n",
      "41:\tlearn: 0.0996277\ttest: 0.0995929\tbest: 0.0995929 (41)\ttotal: 15.7s\tremaining: 40.3s\n",
      "42:\tlearn: 0.0992064\ttest: 0.0991717\tbest: 0.0991717 (42)\ttotal: 16s\tremaining: 39.7s\n",
      "43:\tlearn: 0.0987241\ttest: 0.0986927\tbest: 0.0986927 (43)\ttotal: 16.3s\tremaining: 39.2s\n",
      "44:\tlearn: 0.0983695\ttest: 0.0983354\tbest: 0.0983354 (44)\ttotal: 16.6s\tremaining: 38.8s\n",
      "45:\tlearn: 0.0981003\ttest: 0.0980650\tbest: 0.0980650 (45)\ttotal: 16.9s\tremaining: 38.2s\n",
      "46:\tlearn: 0.0977650\ttest: 0.0977233\tbest: 0.0977233 (46)\ttotal: 17.3s\tremaining: 37.8s\n",
      "47:\tlearn: 0.0974036\ttest: 0.0973642\tbest: 0.0973642 (47)\ttotal: 17.6s\tremaining: 37.4s\n",
      "48:\tlearn: 0.0971087\ttest: 0.0970724\tbest: 0.0970724 (48)\ttotal: 18s\tremaining: 37s\n",
      "49:\tlearn: 0.0967468\ttest: 0.0967104\tbest: 0.0967104 (49)\ttotal: 18.2s\tremaining: 36.5s\n",
      "50:\tlearn: 0.0964693\ttest: 0.0964358\tbest: 0.0964358 (50)\ttotal: 18.6s\tremaining: 36s\n",
      "51:\tlearn: 0.0962472\ttest: 0.0962111\tbest: 0.0962111 (51)\ttotal: 18.9s\tremaining: 35.6s\n",
      "52:\tlearn: 0.0957698\ttest: 0.0957314\tbest: 0.0957314 (52)\ttotal: 19.3s\tremaining: 35.3s\n",
      "53:\tlearn: 0.0954788\ttest: 0.0954437\tbest: 0.0954437 (53)\ttotal: 19.6s\tremaining: 34.9s\n",
      "54:\tlearn: 0.0952432\ttest: 0.0952060\tbest: 0.0952060 (54)\ttotal: 19.9s\tremaining: 34.4s\n",
      "55:\tlearn: 0.0949811\ttest: 0.0949437\tbest: 0.0949437 (55)\ttotal: 20.3s\tremaining: 34s\n",
      "56:\tlearn: 0.0946489\ttest: 0.0946092\tbest: 0.0946092 (56)\ttotal: 20.6s\tremaining: 33.6s\n",
      "57:\tlearn: 0.0943610\ttest: 0.0943272\tbest: 0.0943272 (57)\ttotal: 21s\tremaining: 33.3s\n",
      "58:\tlearn: 0.0941838\ttest: 0.0941486\tbest: 0.0941486 (58)\ttotal: 21.3s\tremaining: 32.9s\n",
      "59:\tlearn: 0.0939432\ttest: 0.0939064\tbest: 0.0939064 (59)\ttotal: 21.6s\tremaining: 32.4s\n",
      "60:\tlearn: 0.0937883\ttest: 0.0937513\tbest: 0.0937513 (60)\ttotal: 21.9s\tremaining: 32s\n",
      "61:\tlearn: 0.0935737\ttest: 0.0935381\tbest: 0.0935381 (61)\ttotal: 22.2s\tremaining: 31.5s\n",
      "62:\tlearn: 0.0934455\ttest: 0.0934080\tbest: 0.0934080 (62)\ttotal: 22.5s\tremaining: 31.1s\n",
      "63:\tlearn: 0.0931649\ttest: 0.0931262\tbest: 0.0931262 (63)\ttotal: 22.9s\tremaining: 30.8s\n",
      "64:\tlearn: 0.0930324\ttest: 0.0929907\tbest: 0.0929907 (64)\ttotal: 23.2s\tremaining: 30.4s\n",
      "65:\tlearn: 0.0928948\ttest: 0.0928561\tbest: 0.0928561 (65)\ttotal: 23.5s\tremaining: 30s\n",
      "66:\tlearn: 0.0926630\ttest: 0.0926214\tbest: 0.0926214 (66)\ttotal: 23.9s\tremaining: 29.6s\n",
      "67:\tlearn: 0.0923134\ttest: 0.0922721\tbest: 0.0922721 (67)\ttotal: 24.3s\tremaining: 29.3s\n",
      "68:\tlearn: 0.0920376\ttest: 0.0920000\tbest: 0.0920000 (68)\ttotal: 24.7s\tremaining: 28.9s\n",
      "69:\tlearn: 0.0919258\ttest: 0.0918886\tbest: 0.0918886 (69)\ttotal: 25s\tremaining: 28.5s\n",
      "70:\tlearn: 0.0918027\ttest: 0.0917649\tbest: 0.0917649 (70)\ttotal: 25.3s\tremaining: 28.2s\n",
      "71:\tlearn: 0.0917283\ttest: 0.0916924\tbest: 0.0916924 (71)\ttotal: 25.6s\tremaining: 27.7s\n",
      "72:\tlearn: 0.0916590\ttest: 0.0916239\tbest: 0.0916239 (72)\ttotal: 25.8s\tremaining: 27.2s\n",
      "73:\tlearn: 0.0915285\ttest: 0.0914936\tbest: 0.0914936 (73)\ttotal: 26.1s\tremaining: 26.8s\n",
      "74:\tlearn: 0.0913314\ttest: 0.0912956\tbest: 0.0912956 (74)\ttotal: 26.4s\tremaining: 26.4s\n",
      "75:\tlearn: 0.0912206\ttest: 0.0911845\tbest: 0.0911845 (75)\ttotal: 26.7s\tremaining: 26s\n",
      "76:\tlearn: 0.0909848\ttest: 0.0909512\tbest: 0.0909512 (76)\ttotal: 27s\tremaining: 25.6s\n",
      "77:\tlearn: 0.0908412\ttest: 0.0908068\tbest: 0.0908068 (77)\ttotal: 27.4s\tremaining: 25.3s\n",
      "78:\tlearn: 0.0907450\ttest: 0.0907128\tbest: 0.0907128 (78)\ttotal: 27.7s\tremaining: 24.9s\n",
      "79:\tlearn: 0.0905574\ttest: 0.0905247\tbest: 0.0905247 (79)\ttotal: 28.1s\tremaining: 24.6s\n",
      "80:\tlearn: 0.0903544\ttest: 0.0903220\tbest: 0.0903220 (80)\ttotal: 28.4s\tremaining: 24.2s\n",
      "81:\tlearn: 0.0901872\ttest: 0.0901579\tbest: 0.0901579 (81)\ttotal: 28.8s\tremaining: 23.9s\n",
      "82:\tlearn: 0.0900583\ttest: 0.0900295\tbest: 0.0900295 (82)\ttotal: 29.1s\tremaining: 23.5s\n",
      "83:\tlearn: 0.0899783\ttest: 0.0899489\tbest: 0.0899489 (83)\ttotal: 29.4s\tremaining: 23.1s\n",
      "84:\tlearn: 0.0898398\ttest: 0.0898089\tbest: 0.0898089 (84)\ttotal: 29.7s\tremaining: 22.7s\n",
      "85:\tlearn: 0.0896888\ttest: 0.0896580\tbest: 0.0896580 (85)\ttotal: 30s\tremaining: 22.4s\n",
      "86:\tlearn: 0.0895850\ttest: 0.0895540\tbest: 0.0895540 (86)\ttotal: 30.3s\tremaining: 21.9s\n",
      "87:\tlearn: 0.0894468\ttest: 0.0894202\tbest: 0.0894202 (87)\ttotal: 30.7s\tremaining: 21.6s\n",
      "88:\tlearn: 0.0892575\ttest: 0.0892366\tbest: 0.0892366 (88)\ttotal: 31s\tremaining: 21.2s\n",
      "89:\tlearn: 0.0891244\ttest: 0.0891037\tbest: 0.0891037 (89)\ttotal: 31.4s\tremaining: 20.9s\n",
      "90:\tlearn: 0.0890305\ttest: 0.0890062\tbest: 0.0890062 (90)\ttotal: 31.6s\tremaining: 20.5s\n",
      "91:\tlearn: 0.0889194\ttest: 0.0888970\tbest: 0.0888970 (91)\ttotal: 32s\tremaining: 20.2s\n",
      "92:\tlearn: 0.0887018\ttest: 0.0886810\tbest: 0.0886810 (92)\ttotal: 32.3s\tremaining: 19.8s\n",
      "93:\tlearn: 0.0885722\ttest: 0.0885515\tbest: 0.0885515 (93)\ttotal: 32.8s\tremaining: 19.5s\n",
      "94:\tlearn: 0.0884673\ttest: 0.0884454\tbest: 0.0884454 (94)\ttotal: 33.1s\tremaining: 19.2s\n",
      "95:\tlearn: 0.0883592\ttest: 0.0883441\tbest: 0.0883441 (95)\ttotal: 33.4s\tremaining: 18.8s\n",
      "96:\tlearn: 0.0882855\ttest: 0.0882704\tbest: 0.0882704 (96)\ttotal: 33.7s\tremaining: 18.4s\n",
      "97:\tlearn: 0.0882209\ttest: 0.0882052\tbest: 0.0882052 (97)\ttotal: 34s\tremaining: 18s\n",
      "98:\tlearn: 0.0880626\ttest: 0.0880520\tbest: 0.0880520 (98)\ttotal: 34.3s\tremaining: 17.7s\n",
      "99:\tlearn: 0.0879453\ttest: 0.0879360\tbest: 0.0879360 (99)\ttotal: 34.7s\tremaining: 17.3s\n",
      "100:\tlearn: 0.0878558\ttest: 0.0878473\tbest: 0.0878473 (100)\ttotal: 34.9s\tremaining: 17s\n",
      "101:\tlearn: 0.0877812\ttest: 0.0877736\tbest: 0.0877736 (101)\ttotal: 35.3s\tremaining: 16.6s\n",
      "102:\tlearn: 0.0876700\ttest: 0.0876662\tbest: 0.0876662 (102)\ttotal: 35.6s\tremaining: 16.2s\n",
      "103:\tlearn: 0.0875139\ttest: 0.0875167\tbest: 0.0875167 (103)\ttotal: 35.9s\tremaining: 15.9s\n",
      "104:\tlearn: 0.0874286\ttest: 0.0874334\tbest: 0.0874334 (104)\ttotal: 36.3s\tremaining: 15.5s\n",
      "105:\tlearn: 0.0873449\ttest: 0.0873459\tbest: 0.0873459 (105)\ttotal: 36.6s\tremaining: 15.2s\n",
      "106:\tlearn: 0.0872768\ttest: 0.0872774\tbest: 0.0872774 (106)\ttotal: 36.9s\tremaining: 14.8s\n",
      "107:\tlearn: 0.0872243\ttest: 0.0872271\tbest: 0.0872271 (107)\ttotal: 37.2s\tremaining: 14.4s\n",
      "108:\tlearn: 0.0871478\ttest: 0.0871486\tbest: 0.0871486 (108)\ttotal: 37.5s\tremaining: 14.1s\n",
      "109:\tlearn: 0.0870616\ttest: 0.0870615\tbest: 0.0870615 (109)\ttotal: 37.8s\tremaining: 13.8s\n",
      "110:\tlearn: 0.0869122\ttest: 0.0869095\tbest: 0.0869095 (110)\ttotal: 38.2s\tremaining: 13.4s\n",
      "111:\tlearn: 0.0868378\ttest: 0.0868344\tbest: 0.0868344 (111)\ttotal: 38.5s\tremaining: 13.1s\n",
      "112:\tlearn: 0.0867867\ttest: 0.0867833\tbest: 0.0867833 (112)\ttotal: 38.8s\tremaining: 12.7s\n",
      "113:\tlearn: 0.0866601\ttest: 0.0866567\tbest: 0.0866567 (113)\ttotal: 39.1s\tremaining: 12.4s\n",
      "114:\tlearn: 0.0865890\ttest: 0.0865852\tbest: 0.0865852 (114)\ttotal: 39.5s\tremaining: 12s\n",
      "115:\tlearn: 0.0865473\ttest: 0.0865421\tbest: 0.0865421 (115)\ttotal: 39.8s\tremaining: 11.7s\n",
      "116:\tlearn: 0.0864717\ttest: 0.0864685\tbest: 0.0864685 (116)\ttotal: 40.1s\tremaining: 11.3s\n",
      "117:\tlearn: 0.0864249\ttest: 0.0864212\tbest: 0.0864212 (117)\ttotal: 40.4s\tremaining: 10.9s\n",
      "118:\tlearn: 0.0863518\ttest: 0.0863477\tbest: 0.0863477 (118)\ttotal: 40.7s\tremaining: 10.6s\n",
      "119:\tlearn: 0.0862955\ttest: 0.0862919\tbest: 0.0862919 (119)\ttotal: 41s\tremaining: 10.2s\n",
      "120:\tlearn: 0.0862388\ttest: 0.0862350\tbest: 0.0862350 (120)\ttotal: 41.3s\tremaining: 9.91s\n",
      "121:\tlearn: 0.0862056\ttest: 0.0862025\tbest: 0.0862025 (121)\ttotal: 41.6s\tremaining: 9.54s\n",
      "122:\tlearn: 0.0861544\ttest: 0.0861513\tbest: 0.0861513 (122)\ttotal: 42s\tremaining: 9.21s\n",
      "123:\tlearn: 0.0860479\ttest: 0.0860431\tbest: 0.0860431 (123)\ttotal: 42.3s\tremaining: 8.87s\n",
      "124:\tlearn: 0.0859739\ttest: 0.0859694\tbest: 0.0859694 (124)\ttotal: 42.6s\tremaining: 8.52s\n",
      "125:\tlearn: 0.0859213\ttest: 0.0859193\tbest: 0.0859193 (125)\ttotal: 42.9s\tremaining: 8.16s\n",
      "126:\tlearn: 0.0858648\ttest: 0.0858631\tbest: 0.0858631 (126)\ttotal: 43.2s\tremaining: 7.82s\n",
      "127:\tlearn: 0.0858126\ttest: 0.0858140\tbest: 0.0858140 (127)\ttotal: 43.5s\tremaining: 7.47s\n",
      "128:\tlearn: 0.0857317\ttest: 0.0857324\tbest: 0.0857324 (128)\ttotal: 43.9s\tremaining: 7.14s\n",
      "129:\tlearn: 0.0856870\ttest: 0.0856864\tbest: 0.0856864 (129)\ttotal: 44.3s\tremaining: 6.81s\n",
      "130:\tlearn: 0.0856389\ttest: 0.0856389\tbest: 0.0856389 (130)\ttotal: 44.8s\tremaining: 6.5s\n",
      "131:\tlearn: 0.0855893\ttest: 0.0855882\tbest: 0.0855882 (131)\ttotal: 45.2s\tremaining: 6.17s\n",
      "132:\tlearn: 0.0855425\ttest: 0.0855396\tbest: 0.0855396 (132)\ttotal: 45.6s\tremaining: 5.82s\n",
      "133:\tlearn: 0.0854349\ttest: 0.0854349\tbest: 0.0854349 (133)\ttotal: 45.9s\tremaining: 5.48s\n",
      "134:\tlearn: 0.0854047\ttest: 0.0854064\tbest: 0.0854064 (134)\ttotal: 46.2s\tremaining: 5.13s\n",
      "135:\tlearn: 0.0853569\ttest: 0.0853588\tbest: 0.0853588 (135)\ttotal: 46.5s\tremaining: 4.79s\n",
      "136:\tlearn: 0.0853165\ttest: 0.0853185\tbest: 0.0853185 (136)\ttotal: 46.9s\tremaining: 4.45s\n",
      "137:\tlearn: 0.0852465\ttest: 0.0852505\tbest: 0.0852505 (137)\ttotal: 47.2s\tremaining: 4.1s\n",
      "138:\tlearn: 0.0851821\ttest: 0.0851887\tbest: 0.0851887 (138)\ttotal: 47.5s\tremaining: 3.76s\n",
      "139:\tlearn: 0.0851316\ttest: 0.0851388\tbest: 0.0851388 (139)\ttotal: 47.8s\tremaining: 3.42s\n",
      "140:\tlearn: 0.0850536\ttest: 0.0850627\tbest: 0.0850627 (140)\ttotal: 48.1s\tremaining: 3.07s\n",
      "141:\tlearn: 0.0849699\ttest: 0.0849825\tbest: 0.0849825 (141)\ttotal: 48.5s\tremaining: 2.73s\n",
      "142:\tlearn: 0.0849262\ttest: 0.0849406\tbest: 0.0849406 (142)\ttotal: 48.7s\tremaining: 2.39s\n",
      "143:\tlearn: 0.0848680\ttest: 0.0848835\tbest: 0.0848835 (143)\ttotal: 49.1s\tremaining: 2.04s\n",
      "144:\tlearn: 0.0848099\ttest: 0.0848245\tbest: 0.0848245 (144)\ttotal: 49.4s\tremaining: 1.7s\n",
      "145:\tlearn: 0.0847512\ttest: 0.0847673\tbest: 0.0847673 (145)\ttotal: 49.8s\tremaining: 1.36s\n",
      "146:\tlearn: 0.0846936\ttest: 0.0847102\tbest: 0.0847102 (146)\ttotal: 50.1s\tremaining: 1.02s\n",
      "147:\tlearn: 0.0846570\ttest: 0.0846734\tbest: 0.0846734 (147)\ttotal: 50.5s\tremaining: 682ms\n",
      "148:\tlearn: 0.0846282\ttest: 0.0846464\tbest: 0.0846464 (148)\ttotal: 50.7s\tremaining: 341ms\n",
      "149:\tlearn: 0.0845984\ttest: 0.0846163\tbest: 0.0846163 (149)\ttotal: 51s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.08461632786\n",
      "bestIteration = 149\n",
      "\n",
      "11:\tloss: 0.0846163\tbest: 0.0846163 (11)\ttotal: 6m 59s\tremaining: 2m 19s\n",
      "0:\tlearn: 0.5476282\ttest: 0.5480053\tbest: 0.5480053 (0)\ttotal: 498ms\tremaining: 49.3s\n",
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      "4:\tlearn: 0.4885096\ttest: 0.4888500\tbest: 0.4888500 (4)\ttotal: 2.33s\tremaining: 44.4s\n",
      "5:\tlearn: 0.4748394\ttest: 0.4751705\tbest: 0.4751705 (5)\ttotal: 2.78s\tremaining: 43.5s\n",
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      "7:\tlearn: 0.4488615\ttest: 0.4491714\tbest: 0.4491714 (7)\ttotal: 3.67s\tremaining: 42.2s\n",
      "8:\tlearn: 0.4365119\ttest: 0.4368116\tbest: 0.4368116 (8)\ttotal: 4.17s\tremaining: 42.2s\n",
      "9:\tlearn: 0.4245062\ttest: 0.4247993\tbest: 0.4247993 (9)\ttotal: 4.65s\tremaining: 41.8s\n",
      "10:\tlearn: 0.4128796\ttest: 0.4131638\tbest: 0.4131638 (10)\ttotal: 5.08s\tremaining: 41.1s\n",
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      "12:\tlearn: 0.3906617\ttest: 0.3909360\tbest: 0.3909360 (12)\ttotal: 5.99s\tremaining: 40.1s\n",
      "13:\tlearn: 0.3801449\ttest: 0.3804123\tbest: 0.3804123 (13)\ttotal: 6.44s\tremaining: 39.5s\n",
      "14:\tlearn: 0.3698939\ttest: 0.3701554\tbest: 0.3701554 (14)\ttotal: 6.91s\tremaining: 39.2s\n",
      "15:\tlearn: 0.3599699\ttest: 0.3602278\tbest: 0.3602278 (15)\ttotal: 7.36s\tremaining: 38.6s\n",
      "16:\tlearn: 0.3504299\ttest: 0.3506790\tbest: 0.3506790 (16)\ttotal: 7.8s\tremaining: 38.1s\n",
      "17:\tlearn: 0.3411269\ttest: 0.3413767\tbest: 0.3413767 (17)\ttotal: 8.27s\tremaining: 37.7s\n",
      "18:\tlearn: 0.3321445\ttest: 0.3323926\tbest: 0.3323926 (18)\ttotal: 8.72s\tremaining: 37.2s\n",
      "19:\tlearn: 0.3234595\ttest: 0.3237026\tbest: 0.3237026 (19)\ttotal: 9.19s\tremaining: 36.8s\n",
      "20:\tlearn: 0.3150426\ttest: 0.3152815\tbest: 0.3152815 (20)\ttotal: 9.66s\tremaining: 36.3s\n",
      "21:\tlearn: 0.3069010\ttest: 0.3071336\tbest: 0.3071336 (21)\ttotal: 10.1s\tremaining: 35.9s\n",
      "22:\tlearn: 0.2990047\ttest: 0.2992353\tbest: 0.2992353 (22)\ttotal: 10.6s\tremaining: 35.4s\n",
      "23:\tlearn: 0.2913836\ttest: 0.2916099\tbest: 0.2916099 (23)\ttotal: 11.1s\tremaining: 35.1s\n",
      "24:\tlearn: 0.2840297\ttest: 0.2842543\tbest: 0.2842543 (24)\ttotal: 11.6s\tremaining: 34.7s\n",
      "25:\tlearn: 0.2769510\ttest: 0.2771704\tbest: 0.2771704 (25)\ttotal: 12s\tremaining: 34.1s\n",
      "26:\tlearn: 0.2700779\ttest: 0.2702969\tbest: 0.2702969 (26)\ttotal: 12.5s\tremaining: 33.7s\n",
      "27:\tlearn: 0.2634815\ttest: 0.2636975\tbest: 0.2636975 (27)\ttotal: 12.9s\tremaining: 33.2s\n",
      "28:\tlearn: 0.2570989\ttest: 0.2573118\tbest: 0.2573118 (28)\ttotal: 13.4s\tremaining: 32.7s\n",
      "29:\tlearn: 0.2509210\ttest: 0.2511313\tbest: 0.2511313 (29)\ttotal: 13.8s\tremaining: 32.3s\n",
      "30:\tlearn: 0.2449240\ttest: 0.2451325\tbest: 0.2451325 (30)\ttotal: 14.3s\tremaining: 31.8s\n",
      "31:\tlearn: 0.2391515\ttest: 0.2393525\tbest: 0.2393525 (31)\ttotal: 14.8s\tremaining: 31.3s\n",
      "32:\tlearn: 0.2335763\ttest: 0.2337736\tbest: 0.2337736 (32)\ttotal: 15.2s\tremaining: 30.9s\n",
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      "34:\tlearn: 0.2230186\ttest: 0.2232107\tbest: 0.2232107 (34)\ttotal: 16.1s\tremaining: 29.9s\n",
      "35:\tlearn: 0.2179787\ttest: 0.2181704\tbest: 0.2181704 (35)\ttotal: 16.6s\tremaining: 29.5s\n",
      "36:\tlearn: 0.2131066\ttest: 0.2132935\tbest: 0.2132935 (36)\ttotal: 17s\tremaining: 29s\n",
      "37:\tlearn: 0.2084344\ttest: 0.2086195\tbest: 0.2086195 (37)\ttotal: 17.5s\tremaining: 28.6s\n",
      "38:\tlearn: 0.2039437\ttest: 0.2041252\tbest: 0.2041252 (38)\ttotal: 18s\tremaining: 28.1s\n",
      "39:\tlearn: 0.1995766\ttest: 0.1997546\tbest: 0.1997546 (39)\ttotal: 18.4s\tremaining: 27.6s\n",
      "40:\tlearn: 0.1953850\ttest: 0.1955589\tbest: 0.1955589 (40)\ttotal: 18.9s\tremaining: 27.2s\n",
      "41:\tlearn: 0.1913739\ttest: 0.1915411\tbest: 0.1915411 (41)\ttotal: 19.3s\tremaining: 26.7s\n",
      "42:\tlearn: 0.1874709\ttest: 0.1876367\tbest: 0.1876367 (42)\ttotal: 19.8s\tremaining: 26.2s\n",
      "43:\tlearn: 0.1837446\ttest: 0.1839046\tbest: 0.1839046 (43)\ttotal: 20.2s\tremaining: 25.7s\n",
      "44:\tlearn: 0.1801814\ttest: 0.1803383\tbest: 0.1803383 (44)\ttotal: 20.7s\tremaining: 25.2s\n",
      "45:\tlearn: 0.1766919\ttest: 0.1768477\tbest: 0.1768477 (45)\ttotal: 21.1s\tremaining: 24.8s\n",
      "46:\tlearn: 0.1733332\ttest: 0.1734849\tbest: 0.1734849 (46)\ttotal: 21.6s\tremaining: 24.3s\n",
      "47:\tlearn: 0.1700722\ttest: 0.1702197\tbest: 0.1702197 (47)\ttotal: 22.1s\tremaining: 23.9s\n",
      "48:\tlearn: 0.1669957\ttest: 0.1671403\tbest: 0.1671403 (48)\ttotal: 22.5s\tremaining: 23.4s\n",
      "49:\tlearn: 0.1639578\ttest: 0.1641001\tbest: 0.1641001 (49)\ttotal: 22.9s\tremaining: 22.9s\n",
      "50:\tlearn: 0.1611225\ttest: 0.1612615\tbest: 0.1612615 (50)\ttotal: 23.4s\tremaining: 22.5s\n",
      "51:\tlearn: 0.1583475\ttest: 0.1584862\tbest: 0.1584862 (51)\ttotal: 23.8s\tremaining: 22s\n",
      "52:\tlearn: 0.1557192\ttest: 0.1558534\tbest: 0.1558534 (52)\ttotal: 24.2s\tremaining: 21.5s\n",
      "53:\tlearn: 0.1531420\ttest: 0.1532743\tbest: 0.1532743 (53)\ttotal: 24.7s\tremaining: 21.1s\n",
      "54:\tlearn: 0.1507279\ttest: 0.1508540\tbest: 0.1508540 (54)\ttotal: 25.3s\tremaining: 20.7s\n",
      "55:\tlearn: 0.1484181\ttest: 0.1485393\tbest: 0.1485393 (55)\ttotal: 25.9s\tremaining: 20.4s\n",
      "56:\tlearn: 0.1461592\ttest: 0.1462789\tbest: 0.1462789 (56)\ttotal: 26.5s\tremaining: 20s\n",
      "57:\tlearn: 0.1439836\ttest: 0.1441027\tbest: 0.1441027 (57)\ttotal: 27s\tremaining: 19.5s\n",
      "58:\tlearn: 0.1419218\ttest: 0.1420359\tbest: 0.1420359 (58)\ttotal: 27.4s\tremaining: 19.1s\n",
      "59:\tlearn: 0.1398906\ttest: 0.1400055\tbest: 0.1400055 (59)\ttotal: 27.9s\tremaining: 18.6s\n",
      "60:\tlearn: 0.1379260\ttest: 0.1380397\tbest: 0.1380397 (60)\ttotal: 28.4s\tremaining: 18.1s\n",
      "61:\tlearn: 0.1360709\ttest: 0.1361841\tbest: 0.1361841 (61)\ttotal: 28.8s\tremaining: 17.6s\n",
      "62:\tlearn: 0.1343477\ttest: 0.1344580\tbest: 0.1344580 (62)\ttotal: 29.2s\tremaining: 17.2s\n",
      "63:\tlearn: 0.1325668\ttest: 0.1326761\tbest: 0.1326761 (63)\ttotal: 29.7s\tremaining: 16.7s\n",
      "64:\tlearn: 0.1308617\ttest: 0.1309692\tbest: 0.1309692 (64)\ttotal: 30.1s\tremaining: 16.2s\n",
      "65:\tlearn: 0.1292731\ttest: 0.1293811\tbest: 0.1293811 (65)\ttotal: 30.6s\tremaining: 15.7s\n",
      "66:\tlearn: 0.1278043\ttest: 0.1279077\tbest: 0.1279077 (66)\ttotal: 31s\tremaining: 15.3s\n",
      "67:\tlearn: 0.1264143\ttest: 0.1265134\tbest: 0.1265134 (67)\ttotal: 31.5s\tremaining: 14.8s\n",
      "68:\tlearn: 0.1250163\ttest: 0.1251160\tbest: 0.1251160 (68)\ttotal: 32s\tremaining: 14.4s\n",
      "69:\tlearn: 0.1237388\ttest: 0.1238341\tbest: 0.1238341 (69)\ttotal: 32.4s\tremaining: 13.9s\n",
      "70:\tlearn: 0.1224430\ttest: 0.1225349\tbest: 0.1225349 (70)\ttotal: 32.8s\tremaining: 13.4s\n",
      "71:\tlearn: 0.1211582\ttest: 0.1212491\tbest: 0.1212491 (71)\ttotal: 33.3s\tremaining: 12.9s\n",
      "72:\tlearn: 0.1199683\ttest: 0.1200551\tbest: 0.1200551 (72)\ttotal: 33.7s\tremaining: 12.5s\n",
      "73:\tlearn: 0.1188431\ttest: 0.1189297\tbest: 0.1189297 (73)\ttotal: 34.2s\tremaining: 12s\n",
      "74:\tlearn: 0.1177750\ttest: 0.1178621\tbest: 0.1178621 (74)\ttotal: 34.7s\tremaining: 11.6s\n",
      "75:\tlearn: 0.1167850\ttest: 0.1168689\tbest: 0.1168689 (75)\ttotal: 35.1s\tremaining: 11.1s\n",
      "76:\tlearn: 0.1158331\ttest: 0.1159132\tbest: 0.1159132 (76)\ttotal: 35.5s\tremaining: 10.6s\n",
      "77:\tlearn: 0.1148844\ttest: 0.1149629\tbest: 0.1149629 (77)\ttotal: 36s\tremaining: 10.2s\n",
      "78:\tlearn: 0.1140278\ttest: 0.1141051\tbest: 0.1141051 (78)\ttotal: 36.4s\tremaining: 9.69s\n",
      "79:\tlearn: 0.1131597\ttest: 0.1132372\tbest: 0.1132372 (79)\ttotal: 36.9s\tremaining: 9.22s\n",
      "80:\tlearn: 0.1123672\ttest: 0.1124422\tbest: 0.1124422 (80)\ttotal: 37.3s\tremaining: 8.75s\n",
      "81:\tlearn: 0.1116116\ttest: 0.1116840\tbest: 0.1116840 (81)\ttotal: 37.7s\tremaining: 8.28s\n",
      "82:\tlearn: 0.1108948\ttest: 0.1109650\tbest: 0.1109650 (82)\ttotal: 38.1s\tremaining: 7.81s\n",
      "83:\tlearn: 0.1101549\ttest: 0.1102234\tbest: 0.1102234 (83)\ttotal: 38.6s\tremaining: 7.34s\n",
      "84:\tlearn: 0.1094617\ttest: 0.1095305\tbest: 0.1095305 (84)\ttotal: 39s\tremaining: 6.89s\n",
      "85:\tlearn: 0.1087892\ttest: 0.1088586\tbest: 0.1088586 (85)\ttotal: 39.5s\tremaining: 6.42s\n",
      "86:\tlearn: 0.1081828\ttest: 0.1082498\tbest: 0.1082498 (86)\ttotal: 39.9s\tremaining: 5.96s\n",
      "87:\tlearn: 0.1075902\ttest: 0.1076556\tbest: 0.1076556 (87)\ttotal: 40.3s\tremaining: 5.5s\n",
      "88:\tlearn: 0.1070062\ttest: 0.1070701\tbest: 0.1070701 (88)\ttotal: 40.7s\tremaining: 5.03s\n",
      "89:\tlearn: 0.1064815\ttest: 0.1065436\tbest: 0.1065436 (89)\ttotal: 41.2s\tremaining: 4.58s\n",
      "90:\tlearn: 0.1059681\ttest: 0.1060293\tbest: 0.1060293 (90)\ttotal: 41.6s\tremaining: 4.11s\n",
      "91:\tlearn: 0.1054800\ttest: 0.1055397\tbest: 0.1055397 (91)\ttotal: 42s\tremaining: 3.65s\n",
      "92:\tlearn: 0.1049787\ttest: 0.1050357\tbest: 0.1050357 (92)\ttotal: 42.4s\tremaining: 3.19s\n",
      "93:\tlearn: 0.1045308\ttest: 0.1045859\tbest: 0.1045859 (93)\ttotal: 42.9s\tremaining: 2.74s\n",
      "94:\tlearn: 0.1041150\ttest: 0.1041690\tbest: 0.1041690 (94)\ttotal: 43.3s\tremaining: 2.28s\n",
      "95:\tlearn: 0.1036132\ttest: 0.1036653\tbest: 0.1036653 (95)\ttotal: 43.7s\tremaining: 1.82s\n",
      "96:\tlearn: 0.1031761\ttest: 0.1032271\tbest: 0.1032271 (96)\ttotal: 44.2s\tremaining: 1.36s\n",
      "97:\tlearn: 0.1028060\ttest: 0.1028551\tbest: 0.1028551 (97)\ttotal: 44.5s\tremaining: 909ms\n",
      "98:\tlearn: 0.1024131\ttest: 0.1024619\tbest: 0.1024619 (98)\ttotal: 45s\tremaining: 454ms\n",
      "99:\tlearn: 0.1019846\ttest: 0.1020305\tbest: 0.1020305 (99)\ttotal: 45.4s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.1020305496\n",
      "bestIteration = 99\n",
      "\n",
      "12:\tloss: 0.1020305\tbest: 0.0846163 (11)\ttotal: 7m 45s\tremaining: 1m 47s\n",
      "0:\tlearn: 0.5103702\ttest: 0.5107310\tbest: 0.5107310 (0)\ttotal: 492ms\tremaining: 48.7s\n",
      "1:\tlearn: 0.4626651\ttest: 0.4629889\tbest: 0.4629889 (1)\ttotal: 966ms\tremaining: 47.3s\n",
      "2:\tlearn: 0.4201069\ttest: 0.4203968\tbest: 0.4203968 (2)\ttotal: 1.41s\tremaining: 45.4s\n",
      "3:\tlearn: 0.3818733\ttest: 0.3821387\tbest: 0.3821387 (3)\ttotal: 1.87s\tremaining: 45s\n",
      "4:\tlearn: 0.3479072\ttest: 0.3481385\tbest: 0.3481385 (4)\ttotal: 2.33s\tremaining: 44.3s\n",
      "5:\tlearn: 0.3172731\ttest: 0.3174850\tbest: 0.3174850 (5)\ttotal: 2.79s\tremaining: 43.7s\n",
      "6:\tlearn: 0.2901624\ttest: 0.2903598\tbest: 0.2903598 (6)\ttotal: 3.26s\tremaining: 43.4s\n",
      "7:\tlearn: 0.2658757\ttest: 0.2660655\tbest: 0.2660655 (7)\ttotal: 3.72s\tremaining: 42.8s\n",
      "8:\tlearn: 0.2445236\ttest: 0.2446979\tbest: 0.2446979 (8)\ttotal: 4.18s\tremaining: 42.3s\n",
      "9:\tlearn: 0.2253785\ttest: 0.2255462\tbest: 0.2255462 (9)\ttotal: 4.61s\tremaining: 41.5s\n",
      "10:\tlearn: 0.2084126\ttest: 0.2085817\tbest: 0.2085817 (10)\ttotal: 5.07s\tremaining: 41s\n",
      "11:\tlearn: 0.1934666\ttest: 0.1936228\tbest: 0.1936228 (11)\ttotal: 5.55s\tremaining: 40.7s\n",
      "12:\tlearn: 0.1803328\ttest: 0.1804892\tbest: 0.1804892 (12)\ttotal: 6.02s\tremaining: 40.3s\n",
      "13:\tlearn: 0.1690301\ttest: 0.1691759\tbest: 0.1691759 (13)\ttotal: 6.47s\tremaining: 39.7s\n",
      "14:\tlearn: 0.1589573\ttest: 0.1590968\tbest: 0.1590968 (14)\ttotal: 6.92s\tremaining: 39.2s\n",
      "15:\tlearn: 0.1502304\ttest: 0.1503672\tbest: 0.1503672 (15)\ttotal: 7.34s\tremaining: 38.5s\n",
      "16:\tlearn: 0.1427963\ttest: 0.1429151\tbest: 0.1429151 (16)\ttotal: 7.79s\tremaining: 38s\n",
      "17:\tlearn: 0.1362566\ttest: 0.1363649\tbest: 0.1363649 (17)\ttotal: 8.23s\tremaining: 37.5s\n",
      "18:\tlearn: 0.1306129\ttest: 0.1307180\tbest: 0.1307180 (18)\ttotal: 8.67s\tremaining: 37s\n",
      "19:\tlearn: 0.1256936\ttest: 0.1257917\tbest: 0.1257917 (19)\ttotal: 9.15s\tremaining: 36.6s\n",
      "20:\tlearn: 0.1211136\ttest: 0.1212065\tbest: 0.1212065 (20)\ttotal: 9.61s\tremaining: 36.2s\n",
      "21:\tlearn: 0.1174548\ttest: 0.1175410\tbest: 0.1175410 (21)\ttotal: 10.1s\tremaining: 35.7s\n",
      "22:\tlearn: 0.1142910\ttest: 0.1143760\tbest: 0.1143760 (22)\ttotal: 10.5s\tremaining: 35.3s\n",
      "23:\tlearn: 0.1114654\ttest: 0.1115349\tbest: 0.1115349 (23)\ttotal: 11s\tremaining: 34.8s\n",
      "24:\tlearn: 0.1091176\ttest: 0.1091830\tbest: 0.1091830 (24)\ttotal: 11.4s\tremaining: 34.3s\n",
      "25:\tlearn: 0.1071551\ttest: 0.1072192\tbest: 0.1072192 (25)\ttotal: 12s\tremaining: 34.1s\n",
      "26:\tlearn: 0.1054510\ttest: 0.1055063\tbest: 0.1055063 (26)\ttotal: 12.5s\tremaining: 33.8s\n",
      "27:\tlearn: 0.1040262\ttest: 0.1040716\tbest: 0.1040716 (27)\ttotal: 13.1s\tremaining: 33.6s\n",
      "28:\tlearn: 0.1024046\ttest: 0.1024454\tbest: 0.1024454 (28)\ttotal: 13.5s\tremaining: 33.2s\n",
      "29:\tlearn: 0.1011520\ttest: 0.1011866\tbest: 0.1011866 (29)\ttotal: 13.9s\tremaining: 32.5s\n",
      "30:\tlearn: 0.1000834\ttest: 0.1001117\tbest: 0.1001117 (30)\ttotal: 14.3s\tremaining: 31.9s\n",
      "31:\tlearn: 0.0991448\ttest: 0.0991674\tbest: 0.0991674 (31)\ttotal: 14.8s\tremaining: 31.4s\n",
      "32:\tlearn: 0.0982233\ttest: 0.0982389\tbest: 0.0982389 (32)\ttotal: 15.2s\tremaining: 30.9s\n",
      "33:\tlearn: 0.0974797\ttest: 0.0974961\tbest: 0.0974961 (33)\ttotal: 15.7s\tremaining: 30.4s\n",
      "34:\tlearn: 0.0967395\ttest: 0.0967483\tbest: 0.0967483 (34)\ttotal: 16.1s\tremaining: 29.8s\n",
      "35:\tlearn: 0.0962294\ttest: 0.0962366\tbest: 0.0962366 (35)\ttotal: 16.5s\tremaining: 29.3s\n",
      "36:\tlearn: 0.0956812\ttest: 0.0956877\tbest: 0.0956877 (36)\ttotal: 16.8s\tremaining: 28.6s\n",
      "37:\tlearn: 0.0950085\ttest: 0.0950120\tbest: 0.0950120 (37)\ttotal: 17.2s\tremaining: 28.1s\n",
      "38:\tlearn: 0.0945551\ttest: 0.0945545\tbest: 0.0945545 (38)\ttotal: 17.6s\tremaining: 27.6s\n",
      "39:\tlearn: 0.0941914\ttest: 0.0941899\tbest: 0.0941899 (39)\ttotal: 18s\tremaining: 27s\n",
      "40:\tlearn: 0.0937169\ttest: 0.0937106\tbest: 0.0937106 (40)\ttotal: 18.4s\tremaining: 26.5s\n",
      "41:\tlearn: 0.0932509\ttest: 0.0932506\tbest: 0.0932506 (41)\ttotal: 18.8s\tremaining: 26s\n",
      "42:\tlearn: 0.0928969\ttest: 0.0928983\tbest: 0.0928983 (42)\ttotal: 19.3s\tremaining: 25.6s\n",
      "43:\tlearn: 0.0923692\ttest: 0.0923584\tbest: 0.0923584 (43)\ttotal: 19.7s\tremaining: 25.1s\n",
      "44:\tlearn: 0.0920615\ttest: 0.0920504\tbest: 0.0920504 (44)\ttotal: 20.1s\tremaining: 24.6s\n",
      "45:\tlearn: 0.0917523\ttest: 0.0917411\tbest: 0.0917411 (45)\ttotal: 20.6s\tremaining: 24.1s\n",
      "46:\tlearn: 0.0915383\ttest: 0.0915247\tbest: 0.0915247 (46)\ttotal: 20.9s\tremaining: 23.6s\n",
      "47:\tlearn: 0.0913286\ttest: 0.0913120\tbest: 0.0913120 (47)\ttotal: 21.3s\tremaining: 23.1s\n",
      "48:\tlearn: 0.0910760\ttest: 0.0910558\tbest: 0.0910558 (48)\ttotal: 21.6s\tremaining: 22.5s\n",
      "49:\tlearn: 0.0906162\ttest: 0.0905919\tbest: 0.0905919 (49)\ttotal: 22.1s\tremaining: 22.1s\n",
      "50:\tlearn: 0.0903809\ttest: 0.0903607\tbest: 0.0903607 (50)\ttotal: 22.6s\tremaining: 21.7s\n",
      "51:\tlearn: 0.0901578\ttest: 0.0901371\tbest: 0.0901371 (51)\ttotal: 22.9s\tremaining: 21.1s\n",
      "52:\tlearn: 0.0898314\ttest: 0.0898149\tbest: 0.0898149 (52)\ttotal: 23.3s\tremaining: 20.7s\n",
      "53:\tlearn: 0.0896644\ttest: 0.0896480\tbest: 0.0896480 (53)\ttotal: 23.7s\tremaining: 20.2s\n",
      "54:\tlearn: 0.0894053\ttest: 0.0893917\tbest: 0.0893917 (54)\ttotal: 24.2s\tremaining: 19.8s\n",
      "55:\tlearn: 0.0892375\ttest: 0.0892262\tbest: 0.0892262 (55)\ttotal: 24.6s\tremaining: 19.3s\n",
      "56:\tlearn: 0.0890304\ttest: 0.0890156\tbest: 0.0890156 (56)\ttotal: 24.9s\tremaining: 18.8s\n",
      "57:\tlearn: 0.0888575\ttest: 0.0888456\tbest: 0.0888456 (57)\ttotal: 25.4s\tremaining: 18.4s\n",
      "58:\tlearn: 0.0887092\ttest: 0.0886985\tbest: 0.0886985 (58)\ttotal: 25.7s\tremaining: 17.9s\n",
      "59:\tlearn: 0.0886002\ttest: 0.0885891\tbest: 0.0885891 (59)\ttotal: 26.1s\tremaining: 17.4s\n",
      "60:\tlearn: 0.0884699\ttest: 0.0884610\tbest: 0.0884610 (60)\ttotal: 26.5s\tremaining: 17s\n",
      "61:\tlearn: 0.0883133\ttest: 0.0883060\tbest: 0.0883060 (61)\ttotal: 26.9s\tremaining: 16.5s\n",
      "62:\tlearn: 0.0881875\ttest: 0.0881839\tbest: 0.0881839 (62)\ttotal: 27.2s\tremaining: 16s\n",
      "63:\tlearn: 0.0879631\ttest: 0.0879554\tbest: 0.0879554 (63)\ttotal: 27.7s\tremaining: 15.6s\n",
      "64:\tlearn: 0.0878094\ttest: 0.0878033\tbest: 0.0878033 (64)\ttotal: 28s\tremaining: 15.1s\n",
      "65:\tlearn: 0.0876852\ttest: 0.0876777\tbest: 0.0876777 (65)\ttotal: 28.3s\tremaining: 14.6s\n",
      "66:\tlearn: 0.0875660\ttest: 0.0875609\tbest: 0.0875609 (66)\ttotal: 28.7s\tremaining: 14.1s\n",
      "67:\tlearn: 0.0874392\ttest: 0.0874335\tbest: 0.0874335 (67)\ttotal: 29.2s\tremaining: 13.7s\n",
      "68:\tlearn: 0.0873698\ttest: 0.0873689\tbest: 0.0873689 (68)\ttotal: 29.4s\tremaining: 13.2s\n",
      "69:\tlearn: 0.0871985\ttest: 0.0871986\tbest: 0.0871986 (69)\ttotal: 29.8s\tremaining: 12.8s\n",
      "70:\tlearn: 0.0870609\ttest: 0.0870630\tbest: 0.0870630 (70)\ttotal: 30.2s\tremaining: 12.3s\n",
      "71:\tlearn: 0.0869630\ttest: 0.0869651\tbest: 0.0869651 (71)\ttotal: 30.5s\tremaining: 11.9s\n",
      "72:\tlearn: 0.0868016\ttest: 0.0868062\tbest: 0.0868062 (72)\ttotal: 30.9s\tremaining: 11.4s\n",
      "73:\tlearn: 0.0866933\ttest: 0.0866980\tbest: 0.0866980 (73)\ttotal: 31.3s\tremaining: 11s\n",
      "74:\tlearn: 0.0864930\ttest: 0.0864984\tbest: 0.0864984 (74)\ttotal: 31.8s\tremaining: 10.6s\n",
      "75:\tlearn: 0.0863696\ttest: 0.0863748\tbest: 0.0863748 (75)\ttotal: 32.2s\tremaining: 10.2s\n",
      "76:\tlearn: 0.0862020\ttest: 0.0862025\tbest: 0.0862025 (76)\ttotal: 32.6s\tremaining: 9.75s\n",
      "77:\tlearn: 0.0861099\ttest: 0.0861115\tbest: 0.0861115 (77)\ttotal: 32.9s\tremaining: 9.29s\n",
      "78:\tlearn: 0.0859969\ttest: 0.0860010\tbest: 0.0860010 (78)\ttotal: 33.3s\tremaining: 8.84s\n",
      "79:\tlearn: 0.0858875\ttest: 0.0858937\tbest: 0.0858937 (79)\ttotal: 33.7s\tremaining: 8.41s\n",
      "80:\tlearn: 0.0858006\ttest: 0.0858096\tbest: 0.0858096 (80)\ttotal: 34.1s\tremaining: 7.99s\n",
      "81:\tlearn: 0.0857063\ttest: 0.0857155\tbest: 0.0857155 (81)\ttotal: 34.5s\tremaining: 7.57s\n",
      "82:\tlearn: 0.0856023\ttest: 0.0856145\tbest: 0.0856145 (82)\ttotal: 34.9s\tremaining: 7.16s\n",
      "83:\tlearn: 0.0854850\ttest: 0.0854984\tbest: 0.0854984 (83)\ttotal: 35.3s\tremaining: 6.73s\n",
      "84:\tlearn: 0.0854241\ttest: 0.0854403\tbest: 0.0854403 (84)\ttotal: 35.7s\tremaining: 6.3s\n",
      "85:\tlearn: 0.0853670\ttest: 0.0853863\tbest: 0.0853863 (85)\ttotal: 36s\tremaining: 5.86s\n",
      "86:\tlearn: 0.0852489\ttest: 0.0852740\tbest: 0.0852740 (86)\ttotal: 36.3s\tremaining: 5.43s\n",
      "87:\tlearn: 0.0851888\ttest: 0.0852149\tbest: 0.0852149 (87)\ttotal: 36.7s\tremaining: 5.01s\n",
      "88:\tlearn: 0.0850823\ttest: 0.0851091\tbest: 0.0851091 (88)\ttotal: 37.1s\tremaining: 4.58s\n",
      "89:\tlearn: 0.0850006\ttest: 0.0850279\tbest: 0.0850279 (89)\ttotal: 37.5s\tremaining: 4.17s\n",
      "90:\tlearn: 0.0849391\ttest: 0.0849665\tbest: 0.0849665 (90)\ttotal: 37.9s\tremaining: 3.75s\n",
      "91:\tlearn: 0.0848910\ttest: 0.0849208\tbest: 0.0849208 (91)\ttotal: 38.3s\tremaining: 3.33s\n",
      "92:\tlearn: 0.0847648\ttest: 0.0847981\tbest: 0.0847981 (92)\ttotal: 38.7s\tremaining: 2.91s\n",
      "93:\tlearn: 0.0847136\ttest: 0.0847448\tbest: 0.0847448 (93)\ttotal: 39s\tremaining: 2.49s\n",
      "94:\tlearn: 0.0846089\ttest: 0.0846430\tbest: 0.0846430 (94)\ttotal: 39.5s\tremaining: 2.08s\n",
      "95:\tlearn: 0.0845504\ttest: 0.0845832\tbest: 0.0845832 (95)\ttotal: 39.9s\tremaining: 1.66s\n",
      "96:\tlearn: 0.0845094\ttest: 0.0845437\tbest: 0.0845437 (96)\ttotal: 40.2s\tremaining: 1.24s\n",
      "97:\tlearn: 0.0844393\ttest: 0.0844748\tbest: 0.0844748 (97)\ttotal: 40.7s\tremaining: 831ms\n",
      "98:\tlearn: 0.0843250\ttest: 0.0843594\tbest: 0.0843594 (98)\ttotal: 41.1s\tremaining: 415ms\n",
      "99:\tlearn: 0.0842697\ttest: 0.0843043\tbest: 0.0843043 (99)\ttotal: 41.5s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.08430434736\n",
      "bestIteration = 99\n",
      "\n",
      "13:\tloss: 0.0843043\tbest: 0.0843043 (13)\ttotal: 8m 26s\tremaining: 1m 12s\n",
      "0:\tlearn: 0.5476282\ttest: 0.5480053\tbest: 0.5480053 (0)\ttotal: 497ms\tremaining: 1m 14s\n",
      "1:\tlearn: 0.5321410\ttest: 0.5325109\tbest: 0.5325109 (1)\ttotal: 929ms\tremaining: 1m 8s\n",
      "2:\tlearn: 0.5171043\ttest: 0.5174636\tbest: 0.5174636 (2)\ttotal: 1.37s\tremaining: 1m 7s\n",
      "3:\tlearn: 0.5025773\ttest: 0.5029252\tbest: 0.5029252 (3)\ttotal: 1.82s\tremaining: 1m 6s\n",
      "4:\tlearn: 0.4885096\ttest: 0.4888500\tbest: 0.4888500 (4)\ttotal: 2.45s\tremaining: 1m 11s\n",
      "5:\tlearn: 0.4748394\ttest: 0.4751705\tbest: 0.4751705 (5)\ttotal: 3.06s\tremaining: 1m 13s\n",
      "6:\tlearn: 0.4616588\ttest: 0.4619791\tbest: 0.4619791 (6)\ttotal: 3.66s\tremaining: 1m 14s\n",
      "7:\tlearn: 0.4488615\ttest: 0.4491714\tbest: 0.4491714 (7)\ttotal: 4.16s\tremaining: 1m 13s\n",
      "8:\tlearn: 0.4365119\ttest: 0.4368116\tbest: 0.4368116 (8)\ttotal: 4.63s\tremaining: 1m 12s\n",
      "9:\tlearn: 0.4245062\ttest: 0.4247993\tbest: 0.4247993 (9)\ttotal: 5.1s\tremaining: 1m 11s\n",
      "10:\tlearn: 0.4128796\ttest: 0.4131638\tbest: 0.4131638 (10)\ttotal: 5.54s\tremaining: 1m 10s\n",
      "11:\tlearn: 0.4015795\ttest: 0.4018588\tbest: 0.4018588 (11)\ttotal: 6.02s\tremaining: 1m 9s\n",
      "12:\tlearn: 0.3906617\ttest: 0.3909360\tbest: 0.3909360 (12)\ttotal: 6.46s\tremaining: 1m 8s\n",
      "13:\tlearn: 0.3801449\ttest: 0.3804123\tbest: 0.3804123 (13)\ttotal: 6.9s\tremaining: 1m 7s\n",
      "14:\tlearn: 0.3698939\ttest: 0.3701554\tbest: 0.3701554 (14)\ttotal: 7.38s\tremaining: 1m 6s\n",
      "15:\tlearn: 0.3599699\ttest: 0.3602278\tbest: 0.3602278 (15)\ttotal: 7.87s\tremaining: 1m 5s\n",
      "16:\tlearn: 0.3504299\ttest: 0.3506790\tbest: 0.3506790 (16)\ttotal: 8.31s\tremaining: 1m 5s\n",
      "17:\tlearn: 0.3411269\ttest: 0.3413767\tbest: 0.3413767 (17)\ttotal: 8.78s\tremaining: 1m 4s\n",
      "18:\tlearn: 0.3321445\ttest: 0.3323926\tbest: 0.3323926 (18)\ttotal: 9.23s\tremaining: 1m 3s\n",
      "19:\tlearn: 0.3234595\ttest: 0.3237026\tbest: 0.3237026 (19)\ttotal: 9.7s\tremaining: 1m 3s\n",
      "20:\tlearn: 0.3150426\ttest: 0.3152815\tbest: 0.3152815 (20)\ttotal: 10.2s\tremaining: 1m 2s\n",
      "21:\tlearn: 0.3069010\ttest: 0.3071336\tbest: 0.3071336 (21)\ttotal: 10.6s\tremaining: 1m 1s\n",
      "22:\tlearn: 0.2990047\ttest: 0.2992353\tbest: 0.2992353 (22)\ttotal: 11.1s\tremaining: 1m 1s\n",
      "23:\tlearn: 0.2913836\ttest: 0.2916099\tbest: 0.2916099 (23)\ttotal: 11.5s\tremaining: 1m\n",
      "24:\tlearn: 0.2840297\ttest: 0.2842543\tbest: 0.2842543 (24)\ttotal: 12s\tremaining: 1m\n",
      "25:\tlearn: 0.2769510\ttest: 0.2771704\tbest: 0.2771704 (25)\ttotal: 12.4s\tremaining: 59.3s\n",
      "26:\tlearn: 0.2700779\ttest: 0.2702969\tbest: 0.2702969 (26)\ttotal: 12.9s\tremaining: 58.8s\n",
      "27:\tlearn: 0.2634815\ttest: 0.2636975\tbest: 0.2636975 (27)\ttotal: 13.4s\tremaining: 58.2s\n",
      "28:\tlearn: 0.2570989\ttest: 0.2573118\tbest: 0.2573118 (28)\ttotal: 13.8s\tremaining: 57.8s\n",
      "29:\tlearn: 0.2509210\ttest: 0.2511313\tbest: 0.2511313 (29)\ttotal: 14.3s\tremaining: 57.3s\n",
      "30:\tlearn: 0.2449240\ttest: 0.2451325\tbest: 0.2451325 (30)\ttotal: 14.8s\tremaining: 56.7s\n",
      "31:\tlearn: 0.2391515\ttest: 0.2393525\tbest: 0.2393525 (31)\ttotal: 15.2s\tremaining: 56.2s\n",
      "32:\tlearn: 0.2335763\ttest: 0.2337736\tbest: 0.2337736 (32)\ttotal: 15.7s\tremaining: 55.8s\n",
      "33:\tlearn: 0.2282197\ttest: 0.2284142\tbest: 0.2284142 (33)\ttotal: 16.2s\tremaining: 55.2s\n",
      "34:\tlearn: 0.2230186\ttest: 0.2232107\tbest: 0.2232107 (34)\ttotal: 16.6s\tremaining: 54.6s\n",
      "35:\tlearn: 0.2179787\ttest: 0.2181704\tbest: 0.2181704 (35)\ttotal: 17.1s\tremaining: 54.1s\n",
      "36:\tlearn: 0.2131066\ttest: 0.2132935\tbest: 0.2132935 (36)\ttotal: 17.5s\tremaining: 53.6s\n",
      "37:\tlearn: 0.2084344\ttest: 0.2086195\tbest: 0.2086195 (37)\ttotal: 18s\tremaining: 53.1s\n",
      "38:\tlearn: 0.2039437\ttest: 0.2041252\tbest: 0.2041252 (38)\ttotal: 18.5s\tremaining: 52.6s\n",
      "39:\tlearn: 0.1995766\ttest: 0.1997546\tbest: 0.1997546 (39)\ttotal: 18.9s\tremaining: 52s\n",
      "40:\tlearn: 0.1953850\ttest: 0.1955589\tbest: 0.1955589 (40)\ttotal: 19.4s\tremaining: 51.6s\n",
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      "44:\tlearn: 0.1801814\ttest: 0.1803383\tbest: 0.1803383 (44)\ttotal: 21.2s\tremaining: 49.4s\n",
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      "46:\tlearn: 0.1733332\ttest: 0.1734849\tbest: 0.1734849 (46)\ttotal: 22s\tremaining: 48.3s\n",
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      "48:\tlearn: 0.1669957\ttest: 0.1671403\tbest: 0.1671403 (48)\ttotal: 23s\tremaining: 47.3s\n",
      "49:\tlearn: 0.1639578\ttest: 0.1641001\tbest: 0.1641001 (49)\ttotal: 23.4s\tremaining: 46.8s\n",
      "50:\tlearn: 0.1611225\ttest: 0.1612615\tbest: 0.1612615 (50)\ttotal: 23.9s\tremaining: 46.4s\n",
      "51:\tlearn: 0.1583475\ttest: 0.1584862\tbest: 0.1584862 (51)\ttotal: 24.3s\tremaining: 45.8s\n",
      "52:\tlearn: 0.1557192\ttest: 0.1558534\tbest: 0.1558534 (52)\ttotal: 24.8s\tremaining: 45.3s\n",
      "53:\tlearn: 0.1531420\ttest: 0.1532743\tbest: 0.1532743 (53)\ttotal: 25.2s\tremaining: 44.8s\n",
      "54:\tlearn: 0.1507279\ttest: 0.1508540\tbest: 0.1508540 (54)\ttotal: 25.7s\tremaining: 44.3s\n",
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      "56:\tlearn: 0.1461592\ttest: 0.1462789\tbest: 0.1462789 (56)\ttotal: 26.6s\tremaining: 43.3s\n",
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      "58:\tlearn: 0.1419218\ttest: 0.1420359\tbest: 0.1420359 (58)\ttotal: 27.5s\tremaining: 42.4s\n",
      "59:\tlearn: 0.1398906\ttest: 0.1400055\tbest: 0.1400055 (59)\ttotal: 27.9s\tremaining: 41.9s\n",
      "60:\tlearn: 0.1379260\ttest: 0.1380397\tbest: 0.1380397 (60)\ttotal: 28.4s\tremaining: 41.4s\n",
      "61:\tlearn: 0.1360709\ttest: 0.1361841\tbest: 0.1361841 (61)\ttotal: 28.8s\tremaining: 40.9s\n",
      "62:\tlearn: 0.1343477\ttest: 0.1344580\tbest: 0.1344580 (62)\ttotal: 29.3s\tremaining: 40.4s\n",
      "63:\tlearn: 0.1325668\ttest: 0.1326761\tbest: 0.1326761 (63)\ttotal: 29.7s\tremaining: 39.9s\n",
      "64:\tlearn: 0.1308617\ttest: 0.1309692\tbest: 0.1309692 (64)\ttotal: 30.2s\tremaining: 39.5s\n",
      "65:\tlearn: 0.1292731\ttest: 0.1293811\tbest: 0.1293811 (65)\ttotal: 30.6s\tremaining: 39s\n",
      "66:\tlearn: 0.1278043\ttest: 0.1279077\tbest: 0.1279077 (66)\ttotal: 31.1s\tremaining: 38.5s\n",
      "67:\tlearn: 0.1264143\ttest: 0.1265134\tbest: 0.1265134 (67)\ttotal: 31.5s\tremaining: 38s\n",
      "68:\tlearn: 0.1250163\ttest: 0.1251160\tbest: 0.1251160 (68)\ttotal: 32s\tremaining: 37.6s\n",
      "69:\tlearn: 0.1237388\ttest: 0.1238341\tbest: 0.1238341 (69)\ttotal: 32.4s\tremaining: 37.1s\n",
      "70:\tlearn: 0.1224430\ttest: 0.1225349\tbest: 0.1225349 (70)\ttotal: 32.8s\tremaining: 36.5s\n",
      "71:\tlearn: 0.1211582\ttest: 0.1212491\tbest: 0.1212491 (71)\ttotal: 33.3s\tremaining: 36.1s\n",
      "72:\tlearn: 0.1199683\ttest: 0.1200551\tbest: 0.1200551 (72)\ttotal: 33.7s\tremaining: 35.6s\n",
      "73:\tlearn: 0.1188431\ttest: 0.1189297\tbest: 0.1189297 (73)\ttotal: 34.3s\tremaining: 35.2s\n",
      "74:\tlearn: 0.1177750\ttest: 0.1178621\tbest: 0.1178621 (74)\ttotal: 34.9s\tremaining: 34.9s\n",
      "75:\tlearn: 0.1167850\ttest: 0.1168689\tbest: 0.1168689 (75)\ttotal: 35.5s\tremaining: 34.6s\n",
      "76:\tlearn: 0.1158331\ttest: 0.1159132\tbest: 0.1159132 (76)\ttotal: 36s\tremaining: 34.1s\n",
      "77:\tlearn: 0.1148844\ttest: 0.1149629\tbest: 0.1149629 (77)\ttotal: 36.5s\tremaining: 33.7s\n",
      "78:\tlearn: 0.1140278\ttest: 0.1141051\tbest: 0.1141051 (78)\ttotal: 36.9s\tremaining: 33.2s\n",
      "79:\tlearn: 0.1131597\ttest: 0.1132372\tbest: 0.1132372 (79)\ttotal: 37.4s\tremaining: 32.7s\n",
      "80:\tlearn: 0.1123672\ttest: 0.1124422\tbest: 0.1124422 (80)\ttotal: 37.8s\tremaining: 32.2s\n",
      "81:\tlearn: 0.1116116\ttest: 0.1116840\tbest: 0.1116840 (81)\ttotal: 38.3s\tremaining: 31.7s\n",
      "82:\tlearn: 0.1108948\ttest: 0.1109650\tbest: 0.1109650 (82)\ttotal: 38.7s\tremaining: 31.2s\n",
      "83:\tlearn: 0.1101549\ttest: 0.1102234\tbest: 0.1102234 (83)\ttotal: 39.1s\tremaining: 30.7s\n",
      "84:\tlearn: 0.1094617\ttest: 0.1095305\tbest: 0.1095305 (84)\ttotal: 39.6s\tremaining: 30.3s\n",
      "85:\tlearn: 0.1087892\ttest: 0.1088586\tbest: 0.1088586 (85)\ttotal: 40s\tremaining: 29.8s\n",
      "86:\tlearn: 0.1081828\ttest: 0.1082498\tbest: 0.1082498 (86)\ttotal: 40.4s\tremaining: 29.3s\n",
      "87:\tlearn: 0.1075902\ttest: 0.1076556\tbest: 0.1076556 (87)\ttotal: 40.9s\tremaining: 28.8s\n",
      "88:\tlearn: 0.1070062\ttest: 0.1070701\tbest: 0.1070701 (88)\ttotal: 41.3s\tremaining: 28.3s\n",
      "89:\tlearn: 0.1064815\ttest: 0.1065436\tbest: 0.1065436 (89)\ttotal: 41.7s\tremaining: 27.8s\n",
      "90:\tlearn: 0.1059681\ttest: 0.1060293\tbest: 0.1060293 (90)\ttotal: 42.1s\tremaining: 27.3s\n",
      "91:\tlearn: 0.1054800\ttest: 0.1055397\tbest: 0.1055397 (91)\ttotal: 42.5s\tremaining: 26.8s\n",
      "92:\tlearn: 0.1049787\ttest: 0.1050357\tbest: 0.1050357 (92)\ttotal: 42.9s\tremaining: 26.3s\n",
      "93:\tlearn: 0.1045308\ttest: 0.1045859\tbest: 0.1045859 (93)\ttotal: 43.4s\tremaining: 25.8s\n",
      "94:\tlearn: 0.1041150\ttest: 0.1041690\tbest: 0.1041690 (94)\ttotal: 43.8s\tremaining: 25.4s\n",
      "95:\tlearn: 0.1036132\ttest: 0.1036653\tbest: 0.1036653 (95)\ttotal: 44.3s\tremaining: 24.9s\n",
      "96:\tlearn: 0.1031761\ttest: 0.1032271\tbest: 0.1032271 (96)\ttotal: 44.7s\tremaining: 24.4s\n",
      "97:\tlearn: 0.1028060\ttest: 0.1028551\tbest: 0.1028551 (97)\ttotal: 45.1s\tremaining: 23.9s\n",
      "98:\tlearn: 0.1024131\ttest: 0.1024619\tbest: 0.1024619 (98)\ttotal: 45.5s\tremaining: 23.4s\n",
      "99:\tlearn: 0.1019846\ttest: 0.1020305\tbest: 0.1020305 (99)\ttotal: 45.9s\tremaining: 23s\n",
      "100:\tlearn: 0.1016257\ttest: 0.1016703\tbest: 0.1016703 (100)\ttotal: 46.3s\tremaining: 22.5s\n",
      "101:\tlearn: 0.1012826\ttest: 0.1013267\tbest: 0.1013267 (101)\ttotal: 46.7s\tremaining: 22s\n",
      "102:\tlearn: 0.1009775\ttest: 0.1010197\tbest: 0.1010197 (102)\ttotal: 47.1s\tremaining: 21.5s\n",
      "103:\tlearn: 0.1006814\ttest: 0.1007219\tbest: 0.1007219 (103)\ttotal: 47.5s\tremaining: 21s\n",
      "104:\tlearn: 0.1003641\ttest: 0.1004045\tbest: 0.1004045 (104)\ttotal: 48s\tremaining: 20.6s\n",
      "105:\tlearn: 0.1000103\ttest: 0.1000484\tbest: 0.1000484 (105)\ttotal: 48.4s\tremaining: 20.1s\n",
      "106:\tlearn: 0.0997381\ttest: 0.0997750\tbest: 0.0997750 (106)\ttotal: 48.8s\tremaining: 19.6s\n",
      "107:\tlearn: 0.0994570\ttest: 0.0994928\tbest: 0.0994928 (107)\ttotal: 49.2s\tremaining: 19.1s\n",
      "108:\tlearn: 0.0991851\ttest: 0.0992195\tbest: 0.0992195 (108)\ttotal: 49.6s\tremaining: 18.6s\n",
      "109:\tlearn: 0.0988300\ttest: 0.0988611\tbest: 0.0988611 (109)\ttotal: 50s\tremaining: 18.2s\n",
      "110:\tlearn: 0.0985654\ttest: 0.0985940\tbest: 0.0985940 (110)\ttotal: 50.5s\tremaining: 17.7s\n",
      "111:\tlearn: 0.0983123\ttest: 0.0983412\tbest: 0.0983412 (111)\ttotal: 50.9s\tremaining: 17.3s\n",
      "112:\tlearn: 0.0980517\ttest: 0.0980769\tbest: 0.0980769 (112)\ttotal: 51.3s\tremaining: 16.8s\n",
      "113:\tlearn: 0.0978231\ttest: 0.0978476\tbest: 0.0978476 (113)\ttotal: 51.7s\tremaining: 16.3s\n",
      "114:\tlearn: 0.0975500\ttest: 0.0975725\tbest: 0.0975725 (114)\ttotal: 52.1s\tremaining: 15.9s\n",
      "115:\tlearn: 0.0972932\ttest: 0.0973147\tbest: 0.0973147 (115)\ttotal: 52.6s\tremaining: 15.4s\n",
      "116:\tlearn: 0.0970908\ttest: 0.0971106\tbest: 0.0971106 (116)\ttotal: 53s\tremaining: 14.9s\n",
      "117:\tlearn: 0.0969074\ttest: 0.0969261\tbest: 0.0969261 (117)\ttotal: 53.4s\tremaining: 14.5s\n",
      "118:\tlearn: 0.0967263\ttest: 0.0967438\tbest: 0.0967438 (118)\ttotal: 53.8s\tremaining: 14s\n",
      "119:\tlearn: 0.0965381\ttest: 0.0965555\tbest: 0.0965555 (119)\ttotal: 54.2s\tremaining: 13.6s\n",
      "120:\tlearn: 0.0963589\ttest: 0.0963765\tbest: 0.0963765 (120)\ttotal: 54.7s\tremaining: 13.1s\n",
      "121:\tlearn: 0.0961824\ttest: 0.0962000\tbest: 0.0962000 (121)\ttotal: 55.1s\tremaining: 12.6s\n",
      "122:\tlearn: 0.0960234\ttest: 0.0960410\tbest: 0.0960410 (122)\ttotal: 55.5s\tremaining: 12.2s\n",
      "123:\tlearn: 0.0958486\ttest: 0.0958649\tbest: 0.0958649 (123)\ttotal: 55.9s\tremaining: 11.7s\n",
      "124:\tlearn: 0.0956756\ttest: 0.0956927\tbest: 0.0956927 (124)\ttotal: 56.3s\tremaining: 11.3s\n",
      "125:\tlearn: 0.0955280\ttest: 0.0955442\tbest: 0.0955442 (125)\ttotal: 56.7s\tremaining: 10.8s\n",
      "126:\tlearn: 0.0953919\ttest: 0.0954078\tbest: 0.0954078 (126)\ttotal: 57.1s\tremaining: 10.3s\n",
      "127:\tlearn: 0.0952201\ttest: 0.0952320\tbest: 0.0952320 (127)\ttotal: 57.5s\tremaining: 9.88s\n",
      "128:\tlearn: 0.0950250\ttest: 0.0950352\tbest: 0.0950352 (128)\ttotal: 57.9s\tremaining: 9.42s\n",
      "129:\tlearn: 0.0948371\ttest: 0.0948453\tbest: 0.0948453 (129)\ttotal: 58.3s\tremaining: 8.96s\n",
      "130:\tlearn: 0.0947040\ttest: 0.0947109\tbest: 0.0947109 (130)\ttotal: 58.6s\tremaining: 8.5s\n",
      "131:\tlearn: 0.0945789\ttest: 0.0945854\tbest: 0.0945854 (131)\ttotal: 59s\tremaining: 8.05s\n",
      "132:\tlearn: 0.0944434\ttest: 0.0944505\tbest: 0.0944505 (132)\ttotal: 59.4s\tremaining: 7.6s\n",
      "133:\tlearn: 0.0943038\ttest: 0.0943117\tbest: 0.0943117 (133)\ttotal: 59.9s\tremaining: 7.15s\n",
      "134:\tlearn: 0.0941714\ttest: 0.0941781\tbest: 0.0941781 (134)\ttotal: 1m\tremaining: 6.69s\n",
      "135:\tlearn: 0.0940180\ttest: 0.0940245\tbest: 0.0940245 (135)\ttotal: 1m\tremaining: 6.25s\n",
      "136:\tlearn: 0.0938982\ttest: 0.0939039\tbest: 0.0939039 (136)\ttotal: 1m 1s\tremaining: 5.79s\n",
      "137:\tlearn: 0.0937647\ttest: 0.0937691\tbest: 0.0937691 (137)\ttotal: 1m 1s\tremaining: 5.34s\n",
      "138:\tlearn: 0.0936579\ttest: 0.0936609\tbest: 0.0936609 (138)\ttotal: 1m 1s\tremaining: 4.89s\n",
      "139:\tlearn: 0.0935520\ttest: 0.0935550\tbest: 0.0935550 (139)\ttotal: 1m 2s\tremaining: 4.44s\n",
      "140:\tlearn: 0.0934519\ttest: 0.0934547\tbest: 0.0934547 (140)\ttotal: 1m 2s\tremaining: 3.99s\n",
      "141:\tlearn: 0.0933059\ttest: 0.0933077\tbest: 0.0933077 (141)\ttotal: 1m 2s\tremaining: 3.54s\n",
      "142:\tlearn: 0.0932054\ttest: 0.0932071\tbest: 0.0932071 (142)\ttotal: 1m 3s\tremaining: 3.1s\n",
      "143:\tlearn: 0.0931029\ttest: 0.0931051\tbest: 0.0931051 (143)\ttotal: 1m 3s\tremaining: 2.66s\n",
      "144:\tlearn: 0.0929390\ttest: 0.0929387\tbest: 0.0929387 (144)\ttotal: 1m 4s\tremaining: 2.21s\n",
      "145:\tlearn: 0.0928494\ttest: 0.0928489\tbest: 0.0928489 (145)\ttotal: 1m 4s\tremaining: 1.77s\n",
      "146:\tlearn: 0.0927482\ttest: 0.0927495\tbest: 0.0927495 (146)\ttotal: 1m 5s\tremaining: 1.33s\n",
      "147:\tlearn: 0.0926048\ttest: 0.0926047\tbest: 0.0926047 (147)\ttotal: 1m 5s\tremaining: 884ms\n",
      "148:\tlearn: 0.0924967\ttest: 0.0924957\tbest: 0.0924957 (148)\ttotal: 1m 5s\tremaining: 442ms\n",
      "149:\tlearn: 0.0923826\ttest: 0.0923821\tbest: 0.0923821 (149)\ttotal: 1m 6s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.0923820838\n",
      "bestIteration = 149\n",
      "\n",
      "14:\tloss: 0.0923821\tbest: 0.0843043 (13)\ttotal: 9m 33s\tremaining: 38.2s\n",
      "0:\tlearn: 0.5103702\ttest: 0.5107310\tbest: 0.5107310 (0)\ttotal: 660ms\tremaining: 1m 38s\n",
      "1:\tlearn: 0.4626651\ttest: 0.4629889\tbest: 0.4629889 (1)\ttotal: 1.28s\tremaining: 1m 34s\n",
      "2:\tlearn: 0.4201069\ttest: 0.4203968\tbest: 0.4203968 (2)\ttotal: 1.72s\tremaining: 1m 24s\n",
      "3:\tlearn: 0.3818733\ttest: 0.3821387\tbest: 0.3821387 (3)\ttotal: 2.23s\tremaining: 1m 21s\n",
      "4:\tlearn: 0.3479072\ttest: 0.3481385\tbest: 0.3481385 (4)\ttotal: 2.68s\tremaining: 1m 17s\n",
      "5:\tlearn: 0.3172731\ttest: 0.3174850\tbest: 0.3174850 (5)\ttotal: 3.14s\tremaining: 1m 15s\n",
      "6:\tlearn: 0.2901624\ttest: 0.2903598\tbest: 0.2903598 (6)\ttotal: 3.6s\tremaining: 1m 13s\n",
      "7:\tlearn: 0.2658757\ttest: 0.2660655\tbest: 0.2660655 (7)\ttotal: 4.06s\tremaining: 1m 12s\n",
      "8:\tlearn: 0.2445236\ttest: 0.2446979\tbest: 0.2446979 (8)\ttotal: 4.51s\tremaining: 1m 10s\n",
      "9:\tlearn: 0.2253785\ttest: 0.2255462\tbest: 0.2255462 (9)\ttotal: 4.94s\tremaining: 1m 9s\n",
      "10:\tlearn: 0.2084126\ttest: 0.2085817\tbest: 0.2085817 (10)\ttotal: 5.39s\tremaining: 1m 8s\n",
      "11:\tlearn: 0.1934666\ttest: 0.1936228\tbest: 0.1936228 (11)\ttotal: 5.83s\tremaining: 1m 7s\n",
      "12:\tlearn: 0.1803328\ttest: 0.1804892\tbest: 0.1804892 (12)\ttotal: 6.29s\tremaining: 1m 6s\n",
      "13:\tlearn: 0.1690301\ttest: 0.1691759\tbest: 0.1691759 (13)\ttotal: 6.73s\tremaining: 1m 5s\n",
      "14:\tlearn: 0.1589573\ttest: 0.1590968\tbest: 0.1590968 (14)\ttotal: 7.22s\tremaining: 1m 5s\n",
      "15:\tlearn: 0.1502304\ttest: 0.1503672\tbest: 0.1503672 (15)\ttotal: 7.64s\tremaining: 1m 4s\n",
      "16:\tlearn: 0.1427963\ttest: 0.1429151\tbest: 0.1429151 (16)\ttotal: 8.1s\tremaining: 1m 3s\n",
      "17:\tlearn: 0.1362566\ttest: 0.1363649\tbest: 0.1363649 (17)\ttotal: 8.54s\tremaining: 1m 2s\n",
      "18:\tlearn: 0.1306129\ttest: 0.1307180\tbest: 0.1307180 (18)\ttotal: 8.98s\tremaining: 1m 1s\n",
      "19:\tlearn: 0.1256936\ttest: 0.1257917\tbest: 0.1257917 (19)\ttotal: 9.46s\tremaining: 1m 1s\n",
      "20:\tlearn: 0.1211136\ttest: 0.1212065\tbest: 0.1212065 (20)\ttotal: 9.94s\tremaining: 1m 1s\n",
      "21:\tlearn: 0.1174548\ttest: 0.1175410\tbest: 0.1175410 (21)\ttotal: 10.4s\tremaining: 1m\n",
      "22:\tlearn: 0.1142910\ttest: 0.1143760\tbest: 0.1143760 (22)\ttotal: 10.9s\tremaining: 1m\n",
      "23:\tlearn: 0.1114654\ttest: 0.1115349\tbest: 0.1115349 (23)\ttotal: 11.3s\tremaining: 59.5s\n",
      "24:\tlearn: 0.1091176\ttest: 0.1091830\tbest: 0.1091830 (24)\ttotal: 11.8s\tremaining: 58.8s\n",
      "25:\tlearn: 0.1071551\ttest: 0.1072192\tbest: 0.1072192 (25)\ttotal: 12.1s\tremaining: 57.9s\n",
      "26:\tlearn: 0.1054510\ttest: 0.1055063\tbest: 0.1055063 (26)\ttotal: 12.5s\tremaining: 57.1s\n",
      "27:\tlearn: 0.1040262\ttest: 0.1040716\tbest: 0.1040716 (27)\ttotal: 13s\tremaining: 56.5s\n",
      "28:\tlearn: 0.1024046\ttest: 0.1024454\tbest: 0.1024454 (28)\ttotal: 13.4s\tremaining: 55.9s\n",
      "29:\tlearn: 0.1011520\ttest: 0.1011866\tbest: 0.1011866 (29)\ttotal: 13.8s\tremaining: 55.1s\n",
      "30:\tlearn: 0.1000834\ttest: 0.1001117\tbest: 0.1001117 (30)\ttotal: 14.2s\tremaining: 54.4s\n",
      "31:\tlearn: 0.0991448\ttest: 0.0991674\tbest: 0.0991674 (31)\ttotal: 14.6s\tremaining: 54s\n",
      "32:\tlearn: 0.0982233\ttest: 0.0982389\tbest: 0.0982389 (32)\ttotal: 15.1s\tremaining: 53.5s\n",
      "33:\tlearn: 0.0974797\ttest: 0.0974961\tbest: 0.0974961 (33)\ttotal: 15.5s\tremaining: 52.8s\n",
      "34:\tlearn: 0.0967395\ttest: 0.0967483\tbest: 0.0967483 (34)\ttotal: 15.9s\tremaining: 52.2s\n",
      "35:\tlearn: 0.0962294\ttest: 0.0962366\tbest: 0.0962366 (35)\ttotal: 16.3s\tremaining: 51.6s\n",
      "36:\tlearn: 0.0956812\ttest: 0.0956877\tbest: 0.0956877 (36)\ttotal: 16.6s\tremaining: 50.8s\n",
      "37:\tlearn: 0.0950085\ttest: 0.0950120\tbest: 0.0950120 (37)\ttotal: 17.1s\tremaining: 50.3s\n",
      "38:\tlearn: 0.0945551\ttest: 0.0945545\tbest: 0.0945545 (38)\ttotal: 17.5s\tremaining: 49.8s\n",
      "39:\tlearn: 0.0941914\ttest: 0.0941899\tbest: 0.0941899 (39)\ttotal: 17.9s\tremaining: 49.1s\n",
      "40:\tlearn: 0.0937169\ttest: 0.0937106\tbest: 0.0937106 (40)\ttotal: 18.3s\tremaining: 48.6s\n",
      "41:\tlearn: 0.0932509\ttest: 0.0932506\tbest: 0.0932506 (41)\ttotal: 18.7s\tremaining: 48.1s\n",
      "42:\tlearn: 0.0928969\ttest: 0.0928983\tbest: 0.0928983 (42)\ttotal: 19.1s\tremaining: 47.6s\n",
      "43:\tlearn: 0.0923692\ttest: 0.0923584\tbest: 0.0923584 (43)\ttotal: 19.6s\tremaining: 47.2s\n",
      "44:\tlearn: 0.0920615\ttest: 0.0920504\tbest: 0.0920504 (44)\ttotal: 20s\tremaining: 46.6s\n",
      "45:\tlearn: 0.0917523\ttest: 0.0917411\tbest: 0.0917411 (45)\ttotal: 20.4s\tremaining: 46.2s\n",
      "46:\tlearn: 0.0915383\ttest: 0.0915247\tbest: 0.0915247 (46)\ttotal: 20.8s\tremaining: 45.6s\n",
      "47:\tlearn: 0.0913286\ttest: 0.0913120\tbest: 0.0913120 (47)\ttotal: 21.2s\tremaining: 45s\n",
      "48:\tlearn: 0.0910760\ttest: 0.0910558\tbest: 0.0910558 (48)\ttotal: 21.5s\tremaining: 44.4s\n",
      "49:\tlearn: 0.0906162\ttest: 0.0905919\tbest: 0.0905919 (49)\ttotal: 22s\tremaining: 44.1s\n",
      "50:\tlearn: 0.0903809\ttest: 0.0903607\tbest: 0.0903607 (50)\ttotal: 22.5s\tremaining: 43.6s\n",
      "51:\tlearn: 0.0901578\ttest: 0.0901371\tbest: 0.0901371 (51)\ttotal: 22.8s\tremaining: 43s\n",
      "52:\tlearn: 0.0898314\ttest: 0.0898149\tbest: 0.0898149 (52)\ttotal: 23.2s\tremaining: 42.5s\n",
      "53:\tlearn: 0.0896644\ttest: 0.0896480\tbest: 0.0896480 (53)\ttotal: 23.6s\tremaining: 42s\n",
      "54:\tlearn: 0.0894053\ttest: 0.0893917\tbest: 0.0893917 (54)\ttotal: 24.1s\tremaining: 41.6s\n",
      "55:\tlearn: 0.0892375\ttest: 0.0892262\tbest: 0.0892262 (55)\ttotal: 24.5s\tremaining: 41.1s\n",
      "56:\tlearn: 0.0890304\ttest: 0.0890156\tbest: 0.0890156 (56)\ttotal: 24.9s\tremaining: 40.6s\n",
      "57:\tlearn: 0.0888575\ttest: 0.0888456\tbest: 0.0888456 (57)\ttotal: 25.3s\tremaining: 40.2s\n",
      "58:\tlearn: 0.0887092\ttest: 0.0886985\tbest: 0.0886985 (58)\ttotal: 25.7s\tremaining: 39.6s\n",
      "59:\tlearn: 0.0886002\ttest: 0.0885891\tbest: 0.0885891 (59)\ttotal: 26s\tremaining: 39s\n",
      "60:\tlearn: 0.0884699\ttest: 0.0884610\tbest: 0.0884610 (60)\ttotal: 26.5s\tremaining: 38.6s\n",
      "61:\tlearn: 0.0883133\ttest: 0.0883060\tbest: 0.0883060 (61)\ttotal: 26.8s\tremaining: 38.1s\n",
      "62:\tlearn: 0.0881875\ttest: 0.0881839\tbest: 0.0881839 (62)\ttotal: 27.2s\tremaining: 37.6s\n",
      "63:\tlearn: 0.0879631\ttest: 0.0879554\tbest: 0.0879554 (63)\ttotal: 27.7s\tremaining: 37.2s\n",
      "64:\tlearn: 0.0878094\ttest: 0.0878033\tbest: 0.0878033 (64)\ttotal: 28s\tremaining: 36.6s\n",
      "65:\tlearn: 0.0876852\ttest: 0.0876777\tbest: 0.0876777 (65)\ttotal: 28.3s\tremaining: 36s\n",
      "66:\tlearn: 0.0875660\ttest: 0.0875609\tbest: 0.0875609 (66)\ttotal: 28.7s\tremaining: 35.6s\n",
      "67:\tlearn: 0.0874392\ttest: 0.0874335\tbest: 0.0874335 (67)\ttotal: 29.2s\tremaining: 35.2s\n",
      "68:\tlearn: 0.0873698\ttest: 0.0873689\tbest: 0.0873689 (68)\ttotal: 29.5s\tremaining: 34.6s\n",
      "69:\tlearn: 0.0871985\ttest: 0.0871986\tbest: 0.0871986 (69)\ttotal: 29.8s\tremaining: 34.1s\n",
      "70:\tlearn: 0.0870609\ttest: 0.0870630\tbest: 0.0870630 (70)\ttotal: 30.2s\tremaining: 33.6s\n",
      "71:\tlearn: 0.0869630\ttest: 0.0869651\tbest: 0.0869651 (71)\ttotal: 30.5s\tremaining: 33.1s\n",
      "72:\tlearn: 0.0868016\ttest: 0.0868062\tbest: 0.0868062 (72)\ttotal: 30.9s\tremaining: 32.6s\n",
      "73:\tlearn: 0.0866933\ttest: 0.0866980\tbest: 0.0866980 (73)\ttotal: 31.4s\tremaining: 32.2s\n",
      "74:\tlearn: 0.0864930\ttest: 0.0864984\tbest: 0.0864984 (74)\ttotal: 31.9s\tremaining: 31.9s\n",
      "75:\tlearn: 0.0863696\ttest: 0.0863748\tbest: 0.0863748 (75)\ttotal: 32.5s\tremaining: 31.6s\n",
      "76:\tlearn: 0.0862020\ttest: 0.0862025\tbest: 0.0862025 (76)\ttotal: 33.1s\tremaining: 31.4s\n",
      "77:\tlearn: 0.0861099\ttest: 0.0861115\tbest: 0.0861115 (77)\ttotal: 33.5s\tremaining: 30.9s\n",
      "78:\tlearn: 0.0859969\ttest: 0.0860010\tbest: 0.0860010 (78)\ttotal: 33.8s\tremaining: 30.4s\n",
      "79:\tlearn: 0.0858875\ttest: 0.0858937\tbest: 0.0858937 (79)\ttotal: 34.2s\tremaining: 29.9s\n",
      "80:\tlearn: 0.0858006\ttest: 0.0858096\tbest: 0.0858096 (80)\ttotal: 34.6s\tremaining: 29.5s\n",
      "81:\tlearn: 0.0857063\ttest: 0.0857155\tbest: 0.0857155 (81)\ttotal: 35s\tremaining: 29.1s\n",
      "82:\tlearn: 0.0856023\ttest: 0.0856145\tbest: 0.0856145 (82)\ttotal: 35.5s\tremaining: 28.7s\n",
      "83:\tlearn: 0.0854850\ttest: 0.0854984\tbest: 0.0854984 (83)\ttotal: 35.9s\tremaining: 28.2s\n",
      "84:\tlearn: 0.0854241\ttest: 0.0854403\tbest: 0.0854403 (84)\ttotal: 36.2s\tremaining: 27.7s\n",
      "85:\tlearn: 0.0853670\ttest: 0.0853863\tbest: 0.0853863 (85)\ttotal: 36.5s\tremaining: 27.2s\n",
      "86:\tlearn: 0.0852489\ttest: 0.0852740\tbest: 0.0852740 (86)\ttotal: 36.9s\tremaining: 26.7s\n",
      "87:\tlearn: 0.0851888\ttest: 0.0852149\tbest: 0.0852149 (87)\ttotal: 37.3s\tremaining: 26.3s\n",
      "88:\tlearn: 0.0850823\ttest: 0.0851091\tbest: 0.0851091 (88)\ttotal: 37.7s\tremaining: 25.8s\n",
      "89:\tlearn: 0.0850006\ttest: 0.0850279\tbest: 0.0850279 (89)\ttotal: 38.1s\tremaining: 25.4s\n",
      "90:\tlearn: 0.0849391\ttest: 0.0849665\tbest: 0.0849665 (90)\ttotal: 38.5s\tremaining: 24.9s\n",
      "91:\tlearn: 0.0848910\ttest: 0.0849208\tbest: 0.0849208 (91)\ttotal: 38.8s\tremaining: 24.5s\n",
      "92:\tlearn: 0.0847648\ttest: 0.0847981\tbest: 0.0847981 (92)\ttotal: 39.2s\tremaining: 24.1s\n",
      "93:\tlearn: 0.0847136\ttest: 0.0847448\tbest: 0.0847448 (93)\ttotal: 39.6s\tremaining: 23.6s\n",
      "94:\tlearn: 0.0846089\ttest: 0.0846430\tbest: 0.0846430 (94)\ttotal: 40s\tremaining: 23.2s\n",
      "95:\tlearn: 0.0845504\ttest: 0.0845832\tbest: 0.0845832 (95)\ttotal: 40.5s\tremaining: 22.8s\n",
      "96:\tlearn: 0.0845094\ttest: 0.0845437\tbest: 0.0845437 (96)\ttotal: 40.8s\tremaining: 22.3s\n",
      "97:\tlearn: 0.0844393\ttest: 0.0844748\tbest: 0.0844748 (97)\ttotal: 41.3s\tremaining: 21.9s\n",
      "98:\tlearn: 0.0843250\ttest: 0.0843594\tbest: 0.0843594 (98)\ttotal: 41.7s\tremaining: 21.5s\n",
      "99:\tlearn: 0.0842697\ttest: 0.0843043\tbest: 0.0843043 (99)\ttotal: 42.1s\tremaining: 21s\n",
      "100:\tlearn: 0.0842294\ttest: 0.0842662\tbest: 0.0842662 (100)\ttotal: 42.4s\tremaining: 20.6s\n",
      "101:\tlearn: 0.0841816\ttest: 0.0842196\tbest: 0.0842196 (101)\ttotal: 42.7s\tremaining: 20.1s\n",
      "102:\tlearn: 0.0841180\ttest: 0.0841574\tbest: 0.0841574 (102)\ttotal: 43.1s\tremaining: 19.7s\n",
      "103:\tlearn: 0.0840712\ttest: 0.0841138\tbest: 0.0841138 (103)\ttotal: 43.5s\tremaining: 19.2s\n",
      "104:\tlearn: 0.0840226\ttest: 0.0840656\tbest: 0.0840656 (104)\ttotal: 43.9s\tremaining: 18.8s\n",
      "105:\tlearn: 0.0839867\ttest: 0.0840316\tbest: 0.0840316 (105)\ttotal: 44.2s\tremaining: 18.4s\n",
      "106:\tlearn: 0.0838908\ttest: 0.0839356\tbest: 0.0839356 (106)\ttotal: 44.6s\tremaining: 17.9s\n",
      "107:\tlearn: 0.0838505\ttest: 0.0838968\tbest: 0.0838968 (107)\ttotal: 45s\tremaining: 17.5s\n",
      "108:\tlearn: 0.0837945\ttest: 0.0838435\tbest: 0.0838435 (108)\ttotal: 45.5s\tremaining: 17.1s\n",
      "109:\tlearn: 0.0837271\ttest: 0.0837753\tbest: 0.0837753 (109)\ttotal: 45.8s\tremaining: 16.7s\n",
      "110:\tlearn: 0.0836886\ttest: 0.0837375\tbest: 0.0837375 (110)\ttotal: 46.2s\tremaining: 16.2s\n",
      "111:\tlearn: 0.0836261\ttest: 0.0836742\tbest: 0.0836742 (111)\ttotal: 46.6s\tremaining: 15.8s\n",
      "112:\tlearn: 0.0835812\ttest: 0.0836301\tbest: 0.0836301 (112)\ttotal: 47.1s\tremaining: 15.4s\n",
      "113:\tlearn: 0.0835118\ttest: 0.0835616\tbest: 0.0835616 (113)\ttotal: 47.5s\tremaining: 15s\n",
      "114:\tlearn: 0.0834702\ttest: 0.0835171\tbest: 0.0835171 (114)\ttotal: 47.9s\tremaining: 14.6s\n",
      "115:\tlearn: 0.0834367\ttest: 0.0834843\tbest: 0.0834843 (115)\ttotal: 48.3s\tremaining: 14.2s\n",
      "116:\tlearn: 0.0834082\ttest: 0.0834550\tbest: 0.0834550 (116)\ttotal: 48.8s\tremaining: 13.8s\n",
      "117:\tlearn: 0.0833363\ttest: 0.0833851\tbest: 0.0833851 (117)\ttotal: 49.2s\tremaining: 13.3s\n",
      "118:\tlearn: 0.0832846\ttest: 0.0833358\tbest: 0.0833358 (118)\ttotal: 49.5s\tremaining: 12.9s\n",
      "119:\tlearn: 0.0832356\ttest: 0.0832899\tbest: 0.0832899 (119)\ttotal: 49.9s\tremaining: 12.5s\n",
      "120:\tlearn: 0.0831977\ttest: 0.0832537\tbest: 0.0832537 (120)\ttotal: 50.4s\tremaining: 12.1s\n",
      "121:\tlearn: 0.0831653\ttest: 0.0832220\tbest: 0.0832220 (121)\ttotal: 50.8s\tremaining: 11.7s\n",
      "122:\tlearn: 0.0831260\ttest: 0.0831830\tbest: 0.0831830 (122)\ttotal: 51.2s\tremaining: 11.2s\n",
      "123:\tlearn: 0.0830937\ttest: 0.0831517\tbest: 0.0831517 (123)\ttotal: 51.6s\tremaining: 10.8s\n",
      "124:\tlearn: 0.0830509\ttest: 0.0831111\tbest: 0.0831111 (124)\ttotal: 52s\tremaining: 10.4s\n",
      "125:\tlearn: 0.0830200\ttest: 0.0830848\tbest: 0.0830848 (125)\ttotal: 52.3s\tremaining: 9.96s\n",
      "126:\tlearn: 0.0829774\ttest: 0.0830437\tbest: 0.0830437 (126)\ttotal: 52.7s\tremaining: 9.55s\n",
      "127:\tlearn: 0.0829434\ttest: 0.0830105\tbest: 0.0830105 (127)\ttotal: 53.1s\tremaining: 9.12s\n",
      "128:\tlearn: 0.0829140\ttest: 0.0829832\tbest: 0.0829832 (128)\ttotal: 53.4s\tremaining: 8.69s\n",
      "129:\tlearn: 0.0828908\ttest: 0.0829590\tbest: 0.0829590 (129)\ttotal: 53.7s\tremaining: 8.26s\n",
      "130:\tlearn: 0.0828540\ttest: 0.0829221\tbest: 0.0829221 (130)\ttotal: 54.1s\tremaining: 7.85s\n",
      "131:\tlearn: 0.0828275\ttest: 0.0828967\tbest: 0.0828967 (131)\ttotal: 54.6s\tremaining: 7.44s\n",
      "132:\tlearn: 0.0828007\ttest: 0.0828702\tbest: 0.0828702 (132)\ttotal: 54.9s\tremaining: 7.01s\n",
      "133:\tlearn: 0.0827796\ttest: 0.0828499\tbest: 0.0828499 (133)\ttotal: 55.3s\tremaining: 6.6s\n",
      "134:\tlearn: 0.0827443\ttest: 0.0828141\tbest: 0.0828141 (134)\ttotal: 55.7s\tremaining: 6.18s\n",
      "135:\tlearn: 0.0827129\ttest: 0.0827870\tbest: 0.0827870 (135)\ttotal: 56s\tremaining: 5.76s\n",
      "136:\tlearn: 0.0826716\ttest: 0.0827462\tbest: 0.0827462 (136)\ttotal: 56.4s\tremaining: 5.35s\n",
      "137:\tlearn: 0.0826275\ttest: 0.0827048\tbest: 0.0827048 (137)\ttotal: 56.8s\tremaining: 4.94s\n",
      "138:\tlearn: 0.0826047\ttest: 0.0826833\tbest: 0.0826833 (138)\ttotal: 57.3s\tremaining: 4.54s\n",
      "139:\tlearn: 0.0825680\ttest: 0.0826470\tbest: 0.0826470 (139)\ttotal: 57.8s\tremaining: 4.13s\n",
      "140:\tlearn: 0.0825426\ttest: 0.0826215\tbest: 0.0826215 (140)\ttotal: 58.2s\tremaining: 3.71s\n",
      "141:\tlearn: 0.0825040\ttest: 0.0825845\tbest: 0.0825845 (141)\ttotal: 58.6s\tremaining: 3.3s\n",
      "142:\tlearn: 0.0824618\ttest: 0.0825392\tbest: 0.0825392 (142)\ttotal: 58.9s\tremaining: 2.88s\n",
      "143:\tlearn: 0.0824365\ttest: 0.0825139\tbest: 0.0825139 (143)\ttotal: 59.3s\tremaining: 2.47s\n",
      "144:\tlearn: 0.0824119\ttest: 0.0824916\tbest: 0.0824916 (144)\ttotal: 59.7s\tremaining: 2.06s\n",
      "145:\tlearn: 0.0823893\ttest: 0.0824680\tbest: 0.0824680 (145)\ttotal: 1m\tremaining: 1.65s\n",
      "146:\tlearn: 0.0823486\ttest: 0.0824279\tbest: 0.0824279 (146)\ttotal: 1m\tremaining: 1.24s\n",
      "147:\tlearn: 0.0823262\ttest: 0.0824052\tbest: 0.0824052 (147)\ttotal: 1m\tremaining: 823ms\n",
      "148:\tlearn: 0.0823084\ttest: 0.0823876\tbest: 0.0823876 (148)\ttotal: 1m 1s\tremaining: 411ms\n",
      "149:\tlearn: 0.0822854\ttest: 0.0823653\tbest: 0.0823653 (149)\ttotal: 1m 1s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.08236530233\n",
      "bestIteration = 149\n",
      "\n",
      "15:\tloss: 0.0823653\tbest: 0.0823653 (15)\ttotal: 10m 35s\tremaining: 0us\n",
      "Estimating final quality...\n",
      "Training on fold [0/3]\n",
      "0:\tlearn: 0.5108180\ttest: 0.5104861\tbest: 0.5104861 (0)\ttotal: 547ms\tremaining: 1m 21s\n",
      "1:\tlearn: 0.4630849\ttest: 0.4627607\tbest: 0.4627607 (1)\ttotal: 1.08s\tremaining: 1m 20s\n",
      "2:\tlearn: 0.4203180\ttest: 0.4199777\tbest: 0.4199777 (2)\ttotal: 1.47s\tremaining: 1m 11s\n",
      "3:\tlearn: 0.3820876\ttest: 0.3817399\tbest: 0.3817399 (3)\ttotal: 1.88s\tremaining: 1m 8s\n",
      "4:\tlearn: 0.3479594\ttest: 0.3476036\tbest: 0.3476036 (4)\ttotal: 2.27s\tremaining: 1m 5s\n",
      "5:\tlearn: 0.3174096\ttest: 0.3170530\tbest: 0.3170530 (5)\ttotal: 2.72s\tremaining: 1m 5s\n",
      "6:\tlearn: 0.2902654\ttest: 0.2899094\tbest: 0.2899094 (6)\ttotal: 3.09s\tremaining: 1m 3s\n",
      "7:\tlearn: 0.2661681\ttest: 0.2658133\tbest: 0.2658133 (7)\ttotal: 3.46s\tremaining: 1m 1s\n",
      "8:\tlearn: 0.2448062\ttest: 0.2444537\tbest: 0.2444537 (8)\ttotal: 3.85s\tremaining: 1m\n",
      "9:\tlearn: 0.2258398\ttest: 0.2254974\tbest: 0.2254974 (9)\ttotal: 4.22s\tremaining: 59.1s\n",
      "10:\tlearn: 0.2088816\ttest: 0.2085467\tbest: 0.2085467 (10)\ttotal: 4.6s\tremaining: 58.1s\n",
      "11:\tlearn: 0.1940176\ttest: 0.1936932\tbest: 0.1936932 (11)\ttotal: 4.97s\tremaining: 57.2s\n",
      "12:\tlearn: 0.1809020\ttest: 0.1805862\tbest: 0.1805862 (12)\ttotal: 5.38s\tremaining: 56.7s\n",
      "13:\tlearn: 0.1693375\ttest: 0.1690234\tbest: 0.1690234 (13)\ttotal: 5.79s\tremaining: 56.2s\n",
      "14:\tlearn: 0.1593113\ttest: 0.1590084\tbest: 0.1590084 (14)\ttotal: 6.17s\tremaining: 55.6s\n",
      "15:\tlearn: 0.1506201\ttest: 0.1503323\tbest: 0.1503323 (15)\ttotal: 6.53s\tremaining: 54.7s\n",
      "16:\tlearn: 0.1428318\ttest: 0.1425477\tbest: 0.1425477 (16)\ttotal: 6.91s\tremaining: 54.1s\n",
      "17:\tlearn: 0.1361902\ttest: 0.1359231\tbest: 0.1359231 (17)\ttotal: 7.3s\tremaining: 53.6s\n",
      "18:\tlearn: 0.1304964\ttest: 0.1302452\tbest: 0.1302452 (18)\ttotal: 7.65s\tremaining: 52.8s\n",
      "19:\tlearn: 0.1255597\ttest: 0.1253197\tbest: 0.1253197 (19)\ttotal: 8s\tremaining: 52s\n",
      "20:\tlearn: 0.1211169\ttest: 0.1208869\tbest: 0.1208869 (20)\ttotal: 8.35s\tremaining: 51.3s\n",
      "21:\tlearn: 0.1175238\ttest: 0.1173119\tbest: 0.1173119 (21)\ttotal: 8.7s\tremaining: 50.6s\n",
      "22:\tlearn: 0.1144955\ttest: 0.1143057\tbest: 0.1143057 (22)\ttotal: 9.07s\tremaining: 50.1s\n",
      "23:\tlearn: 0.1116090\ttest: 0.1114331\tbest: 0.1114331 (23)\ttotal: 9.45s\tremaining: 49.6s\n",
      "24:\tlearn: 0.1092656\ttest: 0.1091045\tbest: 0.1091045 (24)\ttotal: 9.81s\tremaining: 49.1s\n",
      "25:\tlearn: 0.1072140\ttest: 0.1070702\tbest: 0.1070702 (25)\ttotal: 10.2s\tremaining: 48.7s\n",
      "26:\tlearn: 0.1054942\ttest: 0.1053706\tbest: 0.1053706 (26)\ttotal: 10.6s\tremaining: 48.1s\n",
      "27:\tlearn: 0.1040209\ttest: 0.1039164\tbest: 0.1039164 (27)\ttotal: 10.9s\tremaining: 47.5s\n",
      "28:\tlearn: 0.1027145\ttest: 0.1026225\tbest: 0.1026225 (28)\ttotal: 11.3s\tremaining: 47s\n",
      "29:\tlearn: 0.1016486\ttest: 0.1015730\tbest: 0.1015730 (29)\ttotal: 11.6s\tremaining: 46.4s\n",
      "30:\tlearn: 0.1003074\ttest: 0.1002399\tbest: 0.1002399 (30)\ttotal: 12s\tremaining: 46s\n",
      "31:\tlearn: 0.0993140\ttest: 0.0992577\tbest: 0.0992577 (31)\ttotal: 12.4s\tremaining: 45.6s\n",
      "32:\tlearn: 0.0984325\ttest: 0.0983910\tbest: 0.0983910 (32)\ttotal: 12.8s\tremaining: 45.3s\n",
      "33:\tlearn: 0.0977427\ttest: 0.0977131\tbest: 0.0977131 (33)\ttotal: 13.1s\tremaining: 44.8s\n",
      "34:\tlearn: 0.0969632\ttest: 0.0969442\tbest: 0.0969442 (34)\ttotal: 13.5s\tremaining: 44.3s\n",
      "35:\tlearn: 0.0961853\ttest: 0.0961735\tbest: 0.0961735 (35)\ttotal: 13.8s\tremaining: 43.8s\n",
      "36:\tlearn: 0.0956327\ttest: 0.0956314\tbest: 0.0956314 (36)\ttotal: 14.1s\tremaining: 43.2s\n",
      "37:\tlearn: 0.0950493\ttest: 0.0950564\tbest: 0.0950564 (37)\ttotal: 14.5s\tremaining: 42.7s\n",
      "38:\tlearn: 0.0945436\ttest: 0.0945605\tbest: 0.0945605 (38)\ttotal: 14.8s\tremaining: 42.2s\n",
      "39:\tlearn: 0.0940623\ttest: 0.0940848\tbest: 0.0940848 (39)\ttotal: 15.2s\tremaining: 41.9s\n",
      "40:\tlearn: 0.0935726\ttest: 0.0935972\tbest: 0.0935972 (40)\ttotal: 15.6s\tremaining: 41.3s\n",
      "41:\tlearn: 0.0931010\ttest: 0.0931281\tbest: 0.0931281 (41)\ttotal: 15.9s\tremaining: 40.8s\n",
      "42:\tlearn: 0.0928158\ttest: 0.0928529\tbest: 0.0928529 (42)\ttotal: 16.2s\tremaining: 40.3s\n",
      "43:\tlearn: 0.0924539\ttest: 0.0924953\tbest: 0.0924953 (43)\ttotal: 16.6s\tremaining: 39.9s\n",
      "44:\tlearn: 0.0921261\ttest: 0.0921764\tbest: 0.0921764 (44)\ttotal: 16.9s\tremaining: 39.5s\n",
      "45:\tlearn: 0.0918206\ttest: 0.0918799\tbest: 0.0918799 (45)\ttotal: 17.2s\tremaining: 38.9s\n",
      "46:\tlearn: 0.0915914\ttest: 0.0916611\tbest: 0.0916611 (46)\ttotal: 17.5s\tremaining: 38.3s\n",
      "47:\tlearn: 0.0912891\ttest: 0.0913632\tbest: 0.0913632 (47)\ttotal: 17.8s\tremaining: 37.9s\n",
      "48:\tlearn: 0.0910399\ttest: 0.0911145\tbest: 0.0911145 (48)\ttotal: 18.1s\tremaining: 37.4s\n",
      "49:\tlearn: 0.0908262\ttest: 0.0909049\tbest: 0.0909049 (49)\ttotal: 18.5s\tremaining: 37s\n",
      "50:\tlearn: 0.0906790\ttest: 0.0907627\tbest: 0.0907627 (50)\ttotal: 18.8s\tremaining: 36.5s\n",
      "51:\tlearn: 0.0903846\ttest: 0.0904763\tbest: 0.0904763 (51)\ttotal: 19.1s\tremaining: 36s\n",
      "52:\tlearn: 0.0901442\ttest: 0.0902376\tbest: 0.0902376 (52)\ttotal: 19.5s\tremaining: 35.6s\n",
      "53:\tlearn: 0.0899759\ttest: 0.0900713\tbest: 0.0900713 (53)\ttotal: 19.7s\tremaining: 35.1s\n",
      "54:\tlearn: 0.0895920\ttest: 0.0896881\tbest: 0.0896881 (54)\ttotal: 20.1s\tremaining: 34.7s\n",
      "55:\tlearn: 0.0892854\ttest: 0.0893848\tbest: 0.0893848 (55)\ttotal: 20.5s\tremaining: 34.3s\n",
      "56:\tlearn: 0.0890754\ttest: 0.0891739\tbest: 0.0891739 (56)\ttotal: 20.8s\tremaining: 33.9s\n",
      "57:\tlearn: 0.0889063\ttest: 0.0890089\tbest: 0.0890089 (57)\ttotal: 21.1s\tremaining: 33.5s\n",
      "58:\tlearn: 0.0886608\ttest: 0.0887738\tbest: 0.0887738 (58)\ttotal: 21.4s\tremaining: 33s\n",
      "59:\tlearn: 0.0885007\ttest: 0.0886157\tbest: 0.0886157 (59)\ttotal: 21.8s\tremaining: 32.7s\n",
      "60:\tlearn: 0.0883799\ttest: 0.0885034\tbest: 0.0885034 (60)\ttotal: 22.1s\tremaining: 32.2s\n",
      "61:\tlearn: 0.0882421\ttest: 0.0883660\tbest: 0.0883660 (61)\ttotal: 22.4s\tremaining: 31.8s\n",
      "62:\tlearn: 0.0880851\ttest: 0.0882119\tbest: 0.0882119 (62)\ttotal: 22.7s\tremaining: 31.4s\n",
      "63:\tlearn: 0.0879707\ttest: 0.0881003\tbest: 0.0881003 (63)\ttotal: 23.1s\tremaining: 31s\n",
      "64:\tlearn: 0.0878445\ttest: 0.0879770\tbest: 0.0879770 (64)\ttotal: 23.4s\tremaining: 30.6s\n",
      "65:\tlearn: 0.0875971\ttest: 0.0877320\tbest: 0.0877320 (65)\ttotal: 23.7s\tremaining: 30.2s\n",
      "66:\tlearn: 0.0874471\ttest: 0.0875822\tbest: 0.0875822 (66)\ttotal: 24s\tremaining: 29.7s\n",
      "67:\tlearn: 0.0873305\ttest: 0.0874695\tbest: 0.0874695 (67)\ttotal: 24.3s\tremaining: 29.3s\n",
      "68:\tlearn: 0.0871738\ttest: 0.0873085\tbest: 0.0873085 (68)\ttotal: 24.6s\tremaining: 28.9s\n",
      "69:\tlearn: 0.0870229\ttest: 0.0871569\tbest: 0.0871569 (69)\ttotal: 25s\tremaining: 28.5s\n",
      "70:\tlearn: 0.0868281\ttest: 0.0869631\tbest: 0.0869631 (70)\ttotal: 25.3s\tremaining: 28.2s\n",
      "71:\tlearn: 0.0866698\ttest: 0.0868122\tbest: 0.0868122 (71)\ttotal: 25.6s\tremaining: 27.8s\n",
      "72:\tlearn: 0.0865857\ttest: 0.0867289\tbest: 0.0867289 (72)\ttotal: 25.9s\tremaining: 27.3s\n",
      "73:\tlearn: 0.0864898\ttest: 0.0866325\tbest: 0.0866325 (73)\ttotal: 26.3s\tremaining: 27s\n",
      "74:\tlearn: 0.0863770\ttest: 0.0865248\tbest: 0.0865248 (74)\ttotal: 26.5s\tremaining: 26.5s\n",
      "75:\tlearn: 0.0863077\ttest: 0.0864583\tbest: 0.0864583 (75)\ttotal: 26.8s\tremaining: 26.1s\n",
      "76:\tlearn: 0.0862524\ttest: 0.0864056\tbest: 0.0864056 (76)\ttotal: 27s\tremaining: 25.6s\n",
      "77:\tlearn: 0.0861517\ttest: 0.0863059\tbest: 0.0863059 (77)\ttotal: 27.4s\tremaining: 25.3s\n",
      "78:\tlearn: 0.0859963\ttest: 0.0861530\tbest: 0.0861530 (78)\ttotal: 27.8s\tremaining: 24.9s\n",
      "79:\tlearn: 0.0859361\ttest: 0.0860944\tbest: 0.0860944 (79)\ttotal: 28s\tremaining: 24.5s\n",
      "80:\tlearn: 0.0858015\ttest: 0.0859618\tbest: 0.0859618 (80)\ttotal: 28.3s\tremaining: 24.1s\n",
      "81:\tlearn: 0.0856843\ttest: 0.0858450\tbest: 0.0858450 (81)\ttotal: 28.7s\tremaining: 23.8s\n",
      "82:\tlearn: 0.0855750\ttest: 0.0857357\tbest: 0.0857357 (82)\ttotal: 29.1s\tremaining: 23.5s\n",
      "83:\tlearn: 0.0854701\ttest: 0.0856328\tbest: 0.0856328 (83)\ttotal: 29.4s\tremaining: 23.1s\n",
      "84:\tlearn: 0.0854137\ttest: 0.0855788\tbest: 0.0855788 (84)\ttotal: 29.7s\tremaining: 22.7s\n",
      "85:\tlearn: 0.0853488\ttest: 0.0855153\tbest: 0.0855153 (85)\ttotal: 30s\tremaining: 22.3s\n",
      "86:\tlearn: 0.0852450\ttest: 0.0854131\tbest: 0.0854131 (86)\ttotal: 30.4s\tremaining: 22s\n",
      "87:\tlearn: 0.0851067\ttest: 0.0852781\tbest: 0.0852781 (87)\ttotal: 30.7s\tremaining: 21.6s\n",
      "88:\tlearn: 0.0850257\ttest: 0.0851964\tbest: 0.0851964 (88)\ttotal: 31s\tremaining: 21.3s\n",
      "89:\tlearn: 0.0849588\ttest: 0.0851298\tbest: 0.0851298 (89)\ttotal: 31.5s\tremaining: 21s\n",
      "90:\tlearn: 0.0849014\ttest: 0.0850735\tbest: 0.0850735 (90)\ttotal: 31.9s\tremaining: 20.7s\n",
      "91:\tlearn: 0.0848407\ttest: 0.0850122\tbest: 0.0850122 (91)\ttotal: 32.3s\tremaining: 20.4s\n",
      "92:\tlearn: 0.0847920\ttest: 0.0849646\tbest: 0.0849646 (92)\ttotal: 32.8s\tremaining: 20.1s\n",
      "93:\tlearn: 0.0847014\ttest: 0.0848735\tbest: 0.0848735 (93)\ttotal: 33.3s\tremaining: 19.8s\n",
      "94:\tlearn: 0.0846553\ttest: 0.0848303\tbest: 0.0848303 (94)\ttotal: 33.6s\tremaining: 19.4s\n",
      "95:\tlearn: 0.0846049\ttest: 0.0847819\tbest: 0.0847819 (95)\ttotal: 33.9s\tremaining: 19.1s\n",
      "96:\tlearn: 0.0845405\ttest: 0.0847190\tbest: 0.0847190 (96)\ttotal: 34.3s\tremaining: 18.7s\n",
      "97:\tlearn: 0.0844945\ttest: 0.0846755\tbest: 0.0846755 (97)\ttotal: 34.5s\tremaining: 18.3s\n",
      "98:\tlearn: 0.0844187\ttest: 0.0846032\tbest: 0.0846032 (98)\ttotal: 34.8s\tremaining: 18s\n",
      "99:\tlearn: 0.0843263\ttest: 0.0845147\tbest: 0.0845147 (99)\ttotal: 35.2s\tremaining: 17.6s\n",
      "100:\tlearn: 0.0842483\ttest: 0.0844410\tbest: 0.0844410 (100)\ttotal: 35.5s\tremaining: 17.2s\n",
      "101:\tlearn: 0.0841613\ttest: 0.0843516\tbest: 0.0843516 (101)\ttotal: 35.8s\tremaining: 16.9s\n",
      "102:\tlearn: 0.0841158\ttest: 0.0843070\tbest: 0.0843070 (102)\ttotal: 36.2s\tremaining: 16.5s\n",
      "103:\tlearn: 0.0840668\ttest: 0.0842576\tbest: 0.0842576 (103)\ttotal: 36.5s\tremaining: 16.2s\n",
      "104:\tlearn: 0.0839793\ttest: 0.0841696\tbest: 0.0841696 (104)\ttotal: 36.9s\tremaining: 15.8s\n",
      "105:\tlearn: 0.0839351\ttest: 0.0841285\tbest: 0.0841285 (105)\ttotal: 37.2s\tremaining: 15.4s\n",
      "106:\tlearn: 0.0838616\ttest: 0.0840557\tbest: 0.0840557 (106)\ttotal: 37.5s\tremaining: 15.1s\n",
      "107:\tlearn: 0.0838160\ttest: 0.0840107\tbest: 0.0840107 (107)\ttotal: 37.8s\tremaining: 14.7s\n",
      "108:\tlearn: 0.0837523\ttest: 0.0839505\tbest: 0.0839505 (108)\ttotal: 38.1s\tremaining: 14.3s\n",
      "109:\tlearn: 0.0837076\ttest: 0.0839073\tbest: 0.0839073 (109)\ttotal: 38.5s\tremaining: 14s\n",
      "110:\tlearn: 0.0836269\ttest: 0.0838295\tbest: 0.0838295 (110)\ttotal: 38.8s\tremaining: 13.6s\n",
      "111:\tlearn: 0.0835981\ttest: 0.0838020\tbest: 0.0838020 (111)\ttotal: 39.1s\tremaining: 13.3s\n",
      "112:\tlearn: 0.0835520\ttest: 0.0837575\tbest: 0.0837575 (112)\ttotal: 39.4s\tremaining: 12.9s\n",
      "113:\tlearn: 0.0835142\ttest: 0.0837255\tbest: 0.0837255 (113)\ttotal: 39.7s\tremaining: 12.5s\n",
      "114:\tlearn: 0.0834502\ttest: 0.0836601\tbest: 0.0836601 (114)\ttotal: 40.1s\tremaining: 12.2s\n",
      "115:\tlearn: 0.0834118\ttest: 0.0836222\tbest: 0.0836222 (115)\ttotal: 40.4s\tremaining: 11.8s\n",
      "116:\tlearn: 0.0833517\ttest: 0.0835668\tbest: 0.0835668 (116)\ttotal: 40.8s\tremaining: 11.5s\n",
      "117:\tlearn: 0.0832805\ttest: 0.0834966\tbest: 0.0834966 (117)\ttotal: 41.2s\tremaining: 11.2s\n",
      "118:\tlearn: 0.0832358\ttest: 0.0834529\tbest: 0.0834529 (118)\ttotal: 41.5s\tremaining: 10.8s\n",
      "119:\tlearn: 0.0832093\ttest: 0.0834278\tbest: 0.0834278 (119)\ttotal: 41.7s\tremaining: 10.4s\n",
      "120:\tlearn: 0.0831551\ttest: 0.0833746\tbest: 0.0833746 (120)\ttotal: 42.1s\tremaining: 10.1s\n",
      "121:\tlearn: 0.0831150\ttest: 0.0833371\tbest: 0.0833371 (121)\ttotal: 42.3s\tremaining: 9.72s\n",
      "122:\tlearn: 0.0830791\ttest: 0.0833028\tbest: 0.0833028 (122)\ttotal: 42.8s\tremaining: 9.38s\n",
      "123:\tlearn: 0.0830314\ttest: 0.0832562\tbest: 0.0832562 (123)\ttotal: 43.1s\tremaining: 9.05s\n",
      "124:\tlearn: 0.0829878\ttest: 0.0832140\tbest: 0.0832140 (124)\ttotal: 43.5s\tremaining: 8.71s\n",
      "125:\tlearn: 0.0829579\ttest: 0.0831899\tbest: 0.0831899 (125)\ttotal: 43.8s\tremaining: 8.34s\n",
      "126:\tlearn: 0.0829359\ttest: 0.0831684\tbest: 0.0831684 (126)\ttotal: 44.1s\tremaining: 7.99s\n",
      "127:\tlearn: 0.0829120\ttest: 0.0831453\tbest: 0.0831453 (127)\ttotal: 44.4s\tremaining: 7.64s\n",
      "128:\tlearn: 0.0828840\ttest: 0.0831204\tbest: 0.0831204 (128)\ttotal: 44.7s\tremaining: 7.27s\n",
      "129:\tlearn: 0.0828609\ttest: 0.0830980\tbest: 0.0830980 (129)\ttotal: 45s\tremaining: 6.92s\n",
      "130:\tlearn: 0.0828311\ttest: 0.0830697\tbest: 0.0830697 (130)\ttotal: 45.3s\tremaining: 6.57s\n",
      "131:\tlearn: 0.0827938\ttest: 0.0830351\tbest: 0.0830351 (131)\ttotal: 45.7s\tremaining: 6.23s\n",
      "132:\tlearn: 0.0827686\ttest: 0.0830099\tbest: 0.0830099 (132)\ttotal: 46s\tremaining: 5.88s\n",
      "133:\tlearn: 0.0827292\ttest: 0.0829728\tbest: 0.0829728 (133)\ttotal: 46.3s\tremaining: 5.53s\n",
      "134:\tlearn: 0.0826773\ttest: 0.0829214\tbest: 0.0829214 (134)\ttotal: 46.7s\tremaining: 5.18s\n",
      "135:\tlearn: 0.0826340\ttest: 0.0828770\tbest: 0.0828770 (135)\ttotal: 47s\tremaining: 4.84s\n",
      "136:\tlearn: 0.0825929\ttest: 0.0828401\tbest: 0.0828401 (136)\ttotal: 47.3s\tremaining: 4.49s\n",
      "137:\tlearn: 0.0825561\ttest: 0.0828044\tbest: 0.0828044 (137)\ttotal: 47.7s\tremaining: 4.14s\n",
      "138:\tlearn: 0.0825226\ttest: 0.0827721\tbest: 0.0827721 (138)\ttotal: 48.1s\tremaining: 3.81s\n",
      "139:\tlearn: 0.0824966\ttest: 0.0827466\tbest: 0.0827466 (139)\ttotal: 48.5s\tremaining: 3.46s\n",
      "140:\tlearn: 0.0824752\ttest: 0.0827263\tbest: 0.0827263 (140)\ttotal: 48.8s\tremaining: 3.11s\n",
      "141:\tlearn: 0.0824538\ttest: 0.0827069\tbest: 0.0827069 (141)\ttotal: 49.1s\tremaining: 2.77s\n",
      "142:\tlearn: 0.0824339\ttest: 0.0826881\tbest: 0.0826881 (142)\ttotal: 49.4s\tremaining: 2.42s\n",
      "143:\tlearn: 0.0824012\ttest: 0.0826581\tbest: 0.0826581 (143)\ttotal: 49.7s\tremaining: 2.07s\n",
      "144:\tlearn: 0.0823652\ttest: 0.0826248\tbest: 0.0826248 (144)\ttotal: 50s\tremaining: 1.72s\n",
      "145:\tlearn: 0.0823267\ttest: 0.0825883\tbest: 0.0825883 (145)\ttotal: 50.4s\tremaining: 1.38s\n",
      "146:\tlearn: 0.0822920\ttest: 0.0825537\tbest: 0.0825537 (146)\ttotal: 50.8s\tremaining: 1.03s\n",
      "147:\tlearn: 0.0822597\ttest: 0.0825234\tbest: 0.0825234 (147)\ttotal: 51.2s\tremaining: 691ms\n",
      "148:\tlearn: 0.0822346\ttest: 0.0824994\tbest: 0.0824994 (148)\ttotal: 51.5s\tremaining: 345ms\n",
      "149:\tlearn: 0.0822164\ttest: 0.0824819\tbest: 0.0824819 (149)\ttotal: 51.8s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.08248188897\n",
      "bestIteration = 149\n",
      "\n",
      "Training on fold [1/3]\n",
      "0:\tlearn: 0.5106944\ttest: 0.5107777\tbest: 0.5107777 (0)\ttotal: 406ms\tremaining: 1m\n",
      "1:\tlearn: 0.4629657\ttest: 0.4630754\tbest: 0.4630754 (1)\ttotal: 801ms\tremaining: 59.3s\n",
      "2:\tlearn: 0.4201526\ttest: 0.4202819\tbest: 0.4202819 (2)\ttotal: 1.19s\tremaining: 58.3s\n",
      "3:\tlearn: 0.3818817\ttest: 0.3820274\tbest: 0.3820274 (3)\ttotal: 1.59s\tremaining: 57.9s\n",
      "4:\tlearn: 0.3477188\ttest: 0.3478936\tbest: 0.3478936 (4)\ttotal: 1.96s\tremaining: 57s\n",
      "5:\tlearn: 0.3172024\ttest: 0.3173835\tbest: 0.3173835 (5)\ttotal: 2.36s\tremaining: 56.6s\n",
      "6:\tlearn: 0.2900354\ttest: 0.2902236\tbest: 0.2902236 (6)\ttotal: 2.75s\tremaining: 56.2s\n",
      "7:\tlearn: 0.2658287\ttest: 0.2660240\tbest: 0.2660240 (7)\ttotal: 3.15s\tremaining: 55.9s\n",
      "8:\tlearn: 0.2442557\ttest: 0.2444581\tbest: 0.2444581 (8)\ttotal: 3.52s\tremaining: 55.1s\n",
      "9:\tlearn: 0.2252717\ttest: 0.2254854\tbest: 0.2254854 (9)\ttotal: 3.9s\tremaining: 54.7s\n",
      "10:\tlearn: 0.2082881\ttest: 0.2085061\tbest: 0.2085061 (10)\ttotal: 4.28s\tremaining: 54.1s\n",
      "11:\tlearn: 0.1935291\ttest: 0.1937568\tbest: 0.1937568 (11)\ttotal: 4.65s\tremaining: 53.5s\n",
      "12:\tlearn: 0.1803828\ttest: 0.1806142\tbest: 0.1806142 (12)\ttotal: 5.04s\tremaining: 53.1s\n",
      "13:\tlearn: 0.1688891\ttest: 0.1691247\tbest: 0.1691247 (13)\ttotal: 5.43s\tremaining: 52.7s\n",
      "14:\tlearn: 0.1587592\ttest: 0.1589860\tbest: 0.1589860 (14)\ttotal: 5.79s\tremaining: 52.1s\n",
      "15:\tlearn: 0.1499687\ttest: 0.1502034\tbest: 0.1502034 (15)\ttotal: 6.17s\tremaining: 51.7s\n",
      "16:\tlearn: 0.1424333\ttest: 0.1426722\tbest: 0.1426722 (16)\ttotal: 6.54s\tremaining: 51.2s\n",
      "17:\tlearn: 0.1356492\ttest: 0.1358755\tbest: 0.1358755 (17)\ttotal: 6.92s\tremaining: 50.8s\n",
      "18:\tlearn: 0.1299396\ttest: 0.1301591\tbest: 0.1301591 (18)\ttotal: 7.29s\tremaining: 50.3s\n",
      "19:\tlearn: 0.1250413\ttest: 0.1252609\tbest: 0.1252609 (19)\ttotal: 7.75s\tremaining: 50.4s\n",
      "20:\tlearn: 0.1206409\ttest: 0.1208543\tbest: 0.1208543 (20)\ttotal: 8.1s\tremaining: 49.8s\n",
      "21:\tlearn: 0.1169374\ttest: 0.1171458\tbest: 0.1171458 (21)\ttotal: 8.53s\tremaining: 49.6s\n",
      "22:\tlearn: 0.1138649\ttest: 0.1140726\tbest: 0.1140726 (22)\ttotal: 8.89s\tremaining: 49.1s\n",
      "23:\tlearn: 0.1111883\ttest: 0.1113902\tbest: 0.1113902 (23)\ttotal: 9.27s\tremaining: 48.7s\n",
      "24:\tlearn: 0.1089115\ttest: 0.1091093\tbest: 0.1091093 (24)\ttotal: 9.77s\tremaining: 48.8s\n",
      "25:\tlearn: 0.1067981\ttest: 0.1069803\tbest: 0.1069803 (25)\ttotal: 10.2s\tremaining: 48.8s\n",
      "26:\tlearn: 0.1050865\ttest: 0.1052691\tbest: 0.1052691 (26)\ttotal: 10.7s\tremaining: 48.9s\n",
      "27:\tlearn: 0.1035459\ttest: 0.1037216\tbest: 0.1037216 (27)\ttotal: 11.2s\tremaining: 48.8s\n",
      "28:\tlearn: 0.1022894\ttest: 0.1024644\tbest: 0.1024644 (28)\ttotal: 11.5s\tremaining: 48.1s\n",
      "29:\tlearn: 0.1010120\ttest: 0.1011816\tbest: 0.1011816 (29)\ttotal: 11.9s\tremaining: 47.5s\n",
      "30:\tlearn: 0.0999436\ttest: 0.1001072\tbest: 0.1001072 (30)\ttotal: 12.3s\tremaining: 47.1s\n",
      "31:\tlearn: 0.0990574\ttest: 0.0992114\tbest: 0.0992114 (31)\ttotal: 12.6s\tremaining: 46.5s\n",
      "32:\tlearn: 0.0979131\ttest: 0.0980637\tbest: 0.0980637 (32)\ttotal: 13s\tremaining: 46s\n",
      "33:\tlearn: 0.0970779\ttest: 0.0972236\tbest: 0.0972236 (33)\ttotal: 13.3s\tremaining: 45.4s\n",
      "34:\tlearn: 0.0962352\ttest: 0.0963770\tbest: 0.0963770 (34)\ttotal: 13.6s\tremaining: 44.8s\n",
      "35:\tlearn: 0.0956177\ttest: 0.0957521\tbest: 0.0957521 (35)\ttotal: 14s\tremaining: 44.2s\n",
      "36:\tlearn: 0.0948379\ttest: 0.0949626\tbest: 0.0949626 (36)\ttotal: 14.4s\tremaining: 43.9s\n",
      "37:\tlearn: 0.0943850\ttest: 0.0945120\tbest: 0.0945120 (37)\ttotal: 14.7s\tremaining: 43.2s\n",
      "38:\tlearn: 0.0939352\ttest: 0.0940591\tbest: 0.0940591 (38)\ttotal: 15s\tremaining: 42.7s\n",
      "39:\tlearn: 0.0936003\ttest: 0.0937187\tbest: 0.0937187 (39)\ttotal: 15.3s\tremaining: 42.1s\n",
      "40:\tlearn: 0.0931319\ttest: 0.0932511\tbest: 0.0932511 (40)\ttotal: 15.7s\tremaining: 41.7s\n",
      "41:\tlearn: 0.0926923\ttest: 0.0928076\tbest: 0.0928076 (41)\ttotal: 16s\tremaining: 41.2s\n",
      "42:\tlearn: 0.0923093\ttest: 0.0924231\tbest: 0.0924231 (42)\ttotal: 16.4s\tremaining: 40.8s\n",
      "43:\tlearn: 0.0919766\ttest: 0.0920876\tbest: 0.0920876 (43)\ttotal: 16.8s\tremaining: 40.4s\n",
      "44:\tlearn: 0.0916907\ttest: 0.0917986\tbest: 0.0917986 (44)\ttotal: 17.1s\tremaining: 39.8s\n",
      "45:\tlearn: 0.0911963\ttest: 0.0913013\tbest: 0.0913013 (45)\ttotal: 17.5s\tremaining: 39.5s\n",
      "46:\tlearn: 0.0909269\ttest: 0.0910265\tbest: 0.0910265 (46)\ttotal: 17.8s\tremaining: 39.1s\n",
      "47:\tlearn: 0.0906938\ttest: 0.0907935\tbest: 0.0907935 (47)\ttotal: 18.1s\tremaining: 38.5s\n",
      "48:\tlearn: 0.0904808\ttest: 0.0905754\tbest: 0.0905754 (48)\ttotal: 18.4s\tremaining: 38s\n",
      "49:\tlearn: 0.0902384\ttest: 0.0903300\tbest: 0.0903300 (49)\ttotal: 18.8s\tremaining: 37.6s\n",
      "50:\tlearn: 0.0900827\ttest: 0.0901748\tbest: 0.0901748 (50)\ttotal: 19.1s\tremaining: 37s\n",
      "51:\tlearn: 0.0898393\ttest: 0.0899329\tbest: 0.0899329 (51)\ttotal: 19.4s\tremaining: 36.6s\n",
      "52:\tlearn: 0.0895849\ttest: 0.0896794\tbest: 0.0896794 (52)\ttotal: 19.7s\tremaining: 36.1s\n",
      "53:\tlearn: 0.0894134\ttest: 0.0895085\tbest: 0.0895085 (53)\ttotal: 20s\tremaining: 35.6s\n",
      "54:\tlearn: 0.0892121\ttest: 0.0893031\tbest: 0.0893031 (54)\ttotal: 20.4s\tremaining: 35.2s\n",
      "55:\tlearn: 0.0889326\ttest: 0.0890217\tbest: 0.0890217 (55)\ttotal: 20.7s\tremaining: 34.8s\n",
      "56:\tlearn: 0.0887322\ttest: 0.0888196\tbest: 0.0888196 (56)\ttotal: 21.1s\tremaining: 34.5s\n",
      "57:\tlearn: 0.0886125\ttest: 0.0887002\tbest: 0.0887002 (57)\ttotal: 21.5s\tremaining: 34.1s\n",
      "58:\tlearn: 0.0884763\ttest: 0.0885568\tbest: 0.0885568 (58)\ttotal: 21.8s\tremaining: 33.6s\n",
      "59:\tlearn: 0.0883092\ttest: 0.0883872\tbest: 0.0883872 (59)\ttotal: 22.1s\tremaining: 33.2s\n",
      "60:\tlearn: 0.0881735\ttest: 0.0882520\tbest: 0.0882520 (60)\ttotal: 22.4s\tremaining: 32.7s\n",
      "61:\tlearn: 0.0879920\ttest: 0.0880696\tbest: 0.0880696 (61)\ttotal: 22.7s\tremaining: 32.3s\n",
      "62:\tlearn: 0.0878465\ttest: 0.0879265\tbest: 0.0879265 (62)\ttotal: 23.1s\tremaining: 31.9s\n",
      "63:\tlearn: 0.0877394\ttest: 0.0878192\tbest: 0.0878192 (63)\ttotal: 23.4s\tremaining: 31.4s\n",
      "64:\tlearn: 0.0875785\ttest: 0.0876585\tbest: 0.0876585 (64)\ttotal: 23.7s\tremaining: 31s\n",
      "65:\tlearn: 0.0874085\ttest: 0.0874888\tbest: 0.0874888 (65)\ttotal: 24s\tremaining: 30.6s\n",
      "66:\tlearn: 0.0871920\ttest: 0.0872717\tbest: 0.0872717 (66)\ttotal: 24.4s\tremaining: 30.2s\n",
      "67:\tlearn: 0.0871081\ttest: 0.0871876\tbest: 0.0871876 (67)\ttotal: 24.6s\tremaining: 29.7s\n",
      "68:\tlearn: 0.0870080\ttest: 0.0870879\tbest: 0.0870879 (68)\ttotal: 24.9s\tremaining: 29.3s\n",
      "69:\tlearn: 0.0869138\ttest: 0.0869928\tbest: 0.0869928 (69)\ttotal: 25.2s\tremaining: 28.8s\n",
      "70:\tlearn: 0.0867719\ttest: 0.0868482\tbest: 0.0868482 (70)\ttotal: 25.6s\tremaining: 28.5s\n",
      "71:\tlearn: 0.0866711\ttest: 0.0867469\tbest: 0.0867469 (71)\ttotal: 25.9s\tremaining: 28s\n",
      "72:\tlearn: 0.0865899\ttest: 0.0866663\tbest: 0.0866663 (72)\ttotal: 26.2s\tremaining: 27.6s\n",
      "73:\tlearn: 0.0864758\ttest: 0.0865544\tbest: 0.0865544 (73)\ttotal: 26.5s\tremaining: 27.2s\n",
      "74:\tlearn: 0.0864046\ttest: 0.0864803\tbest: 0.0864803 (74)\ttotal: 26.8s\tremaining: 26.8s\n",
      "75:\tlearn: 0.0862392\ttest: 0.0863138\tbest: 0.0863138 (75)\ttotal: 27.1s\tremaining: 26.4s\n",
      "76:\tlearn: 0.0860652\ttest: 0.0861394\tbest: 0.0861394 (76)\ttotal: 27.5s\tremaining: 26.1s\n",
      "77:\tlearn: 0.0859458\ttest: 0.0860185\tbest: 0.0860185 (77)\ttotal: 27.9s\tremaining: 25.7s\n",
      "78:\tlearn: 0.0858488\ttest: 0.0859221\tbest: 0.0859221 (78)\ttotal: 28.3s\tremaining: 25.4s\n",
      "79:\tlearn: 0.0857873\ttest: 0.0858611\tbest: 0.0858611 (79)\ttotal: 28.6s\tremaining: 25s\n",
      "80:\tlearn: 0.0857377\ttest: 0.0858134\tbest: 0.0858134 (80)\ttotal: 28.9s\tremaining: 24.6s\n",
      "81:\tlearn: 0.0855987\ttest: 0.0856749\tbest: 0.0856749 (81)\ttotal: 29.2s\tremaining: 24.2s\n",
      "82:\tlearn: 0.0855107\ttest: 0.0855884\tbest: 0.0855884 (82)\ttotal: 29.5s\tremaining: 23.8s\n",
      "83:\tlearn: 0.0854091\ttest: 0.0854821\tbest: 0.0854821 (83)\ttotal: 29.8s\tremaining: 23.4s\n",
      "84:\tlearn: 0.0853110\ttest: 0.0853897\tbest: 0.0853897 (84)\ttotal: 30.1s\tremaining: 23s\n",
      "85:\tlearn: 0.0852151\ttest: 0.0852970\tbest: 0.0852970 (85)\ttotal: 30.5s\tremaining: 22.7s\n",
      "86:\tlearn: 0.0851155\ttest: 0.0851959\tbest: 0.0851959 (86)\ttotal: 30.9s\tremaining: 22.3s\n",
      "87:\tlearn: 0.0850349\ttest: 0.0851150\tbest: 0.0851150 (87)\ttotal: 31.2s\tremaining: 22s\n",
      "88:\tlearn: 0.0849815\ttest: 0.0850633\tbest: 0.0850633 (88)\ttotal: 31.5s\tremaining: 21.6s\n",
      "89:\tlearn: 0.0848404\ttest: 0.0849216\tbest: 0.0849216 (89)\ttotal: 31.8s\tremaining: 21.2s\n",
      "90:\tlearn: 0.0847993\ttest: 0.0848803\tbest: 0.0848803 (90)\ttotal: 32.1s\tremaining: 20.8s\n",
      "91:\tlearn: 0.0846990\ttest: 0.0847807\tbest: 0.0847807 (91)\ttotal: 32.4s\tremaining: 20.5s\n",
      "92:\tlearn: 0.0845800\ttest: 0.0846617\tbest: 0.0846617 (92)\ttotal: 32.8s\tremaining: 20.1s\n",
      "93:\tlearn: 0.0845155\ttest: 0.0845990\tbest: 0.0845990 (93)\ttotal: 33.1s\tremaining: 19.7s\n",
      "94:\tlearn: 0.0844637\ttest: 0.0845474\tbest: 0.0845474 (94)\ttotal: 33.5s\tremaining: 19.4s\n",
      "95:\tlearn: 0.0844193\ttest: 0.0845057\tbest: 0.0845057 (95)\ttotal: 33.8s\tremaining: 19s\n",
      "96:\tlearn: 0.0843419\ttest: 0.0844273\tbest: 0.0844273 (96)\ttotal: 34.2s\tremaining: 18.7s\n",
      "97:\tlearn: 0.0842732\ttest: 0.0843593\tbest: 0.0843593 (97)\ttotal: 34.5s\tremaining: 18.3s\n",
      "98:\tlearn: 0.0842128\ttest: 0.0842998\tbest: 0.0842998 (98)\ttotal: 34.8s\tremaining: 17.9s\n",
      "99:\tlearn: 0.0841555\ttest: 0.0842450\tbest: 0.0842450 (99)\ttotal: 35.1s\tremaining: 17.6s\n",
      "100:\tlearn: 0.0841063\ttest: 0.0841963\tbest: 0.0841963 (100)\ttotal: 35.5s\tremaining: 17.2s\n",
      "101:\tlearn: 0.0840759\ttest: 0.0841679\tbest: 0.0841679 (101)\ttotal: 35.8s\tremaining: 16.8s\n",
      "102:\tlearn: 0.0840280\ttest: 0.0841154\tbest: 0.0841154 (102)\ttotal: 36.1s\tremaining: 16.4s\n",
      "103:\tlearn: 0.0839901\ttest: 0.0840783\tbest: 0.0840783 (103)\ttotal: 36.4s\tremaining: 16.1s\n",
      "104:\tlearn: 0.0839017\ttest: 0.0839910\tbest: 0.0839910 (104)\ttotal: 36.7s\tremaining: 15.7s\n",
      "105:\tlearn: 0.0838561\ttest: 0.0839478\tbest: 0.0839478 (105)\ttotal: 37s\tremaining: 15.4s\n",
      "106:\tlearn: 0.0837834\ttest: 0.0838764\tbest: 0.0838764 (106)\ttotal: 37.3s\tremaining: 15s\n",
      "107:\tlearn: 0.0837433\ttest: 0.0838369\tbest: 0.0838369 (107)\ttotal: 37.6s\tremaining: 14.6s\n",
      "108:\tlearn: 0.0836872\ttest: 0.0837831\tbest: 0.0837831 (108)\ttotal: 38s\tremaining: 14.3s\n",
      "109:\tlearn: 0.0836244\ttest: 0.0837215\tbest: 0.0837215 (109)\ttotal: 38.4s\tremaining: 14s\n",
      "110:\tlearn: 0.0835623\ttest: 0.0836599\tbest: 0.0836599 (110)\ttotal: 38.7s\tremaining: 13.6s\n",
      "111:\tlearn: 0.0835098\ttest: 0.0836037\tbest: 0.0836037 (111)\ttotal: 39.1s\tremaining: 13.3s\n",
      "112:\tlearn: 0.0834750\ttest: 0.0835707\tbest: 0.0835707 (112)\ttotal: 39.4s\tremaining: 12.9s\n",
      "113:\tlearn: 0.0834439\ttest: 0.0835410\tbest: 0.0835410 (113)\ttotal: 39.7s\tremaining: 12.5s\n",
      "114:\tlearn: 0.0833962\ttest: 0.0834953\tbest: 0.0834953 (114)\ttotal: 40s\tremaining: 12.2s\n",
      "115:\tlearn: 0.0833610\ttest: 0.0834651\tbest: 0.0834651 (115)\ttotal: 40.3s\tremaining: 11.8s\n",
      "116:\tlearn: 0.0833067\ttest: 0.0834137\tbest: 0.0834137 (116)\ttotal: 40.7s\tremaining: 11.5s\n",
      "117:\tlearn: 0.0832706\ttest: 0.0833776\tbest: 0.0833776 (117)\ttotal: 41s\tremaining: 11.1s\n",
      "118:\tlearn: 0.0832356\ttest: 0.0833432\tbest: 0.0833432 (118)\ttotal: 41.3s\tremaining: 10.8s\n",
      "119:\tlearn: 0.0832142\ttest: 0.0833199\tbest: 0.0833199 (119)\ttotal: 41.6s\tremaining: 10.4s\n",
      "120:\tlearn: 0.0831542\ttest: 0.0832612\tbest: 0.0832612 (120)\ttotal: 42.2s\tremaining: 10.1s\n",
      "121:\tlearn: 0.0831106\ttest: 0.0832186\tbest: 0.0832186 (121)\ttotal: 42.7s\tremaining: 9.79s\n",
      "122:\tlearn: 0.0830708\ttest: 0.0831798\tbest: 0.0831798 (122)\ttotal: 43.1s\tremaining: 9.47s\n",
      "123:\tlearn: 0.0830330\ttest: 0.0831435\tbest: 0.0831435 (123)\ttotal: 43.5s\tremaining: 9.11s\n",
      "124:\tlearn: 0.0830012\ttest: 0.0831135\tbest: 0.0831135 (124)\ttotal: 43.8s\tremaining: 8.76s\n",
      "125:\tlearn: 0.0829565\ttest: 0.0830685\tbest: 0.0830685 (125)\ttotal: 44.2s\tremaining: 8.42s\n",
      "126:\tlearn: 0.0828994\ttest: 0.0830120\tbest: 0.0830120 (126)\ttotal: 44.5s\tremaining: 8.06s\n",
      "127:\tlearn: 0.0828605\ttest: 0.0829734\tbest: 0.0829734 (127)\ttotal: 44.9s\tremaining: 7.71s\n",
      "128:\tlearn: 0.0828408\ttest: 0.0829550\tbest: 0.0829550 (128)\ttotal: 45.2s\tremaining: 7.35s\n",
      "129:\tlearn: 0.0827933\ttest: 0.0829107\tbest: 0.0829107 (129)\ttotal: 45.5s\tremaining: 7s\n",
      "130:\tlearn: 0.0827639\ttest: 0.0828831\tbest: 0.0828831 (130)\ttotal: 45.8s\tremaining: 6.64s\n",
      "131:\tlearn: 0.0827157\ttest: 0.0828351\tbest: 0.0828351 (131)\ttotal: 46.1s\tremaining: 6.29s\n",
      "132:\tlearn: 0.0826874\ttest: 0.0828084\tbest: 0.0828084 (132)\ttotal: 46.5s\tremaining: 5.94s\n",
      "133:\tlearn: 0.0826548\ttest: 0.0827769\tbest: 0.0827769 (133)\ttotal: 46.8s\tremaining: 5.58s\n",
      "134:\tlearn: 0.0826228\ttest: 0.0827452\tbest: 0.0827452 (134)\ttotal: 47.1s\tremaining: 5.23s\n",
      "135:\tlearn: 0.0825793\ttest: 0.0827050\tbest: 0.0827050 (135)\ttotal: 47.4s\tremaining: 4.88s\n",
      "136:\tlearn: 0.0825429\ttest: 0.0826692\tbest: 0.0826692 (136)\ttotal: 47.7s\tremaining: 4.53s\n",
      "137:\tlearn: 0.0825012\ttest: 0.0826276\tbest: 0.0826276 (137)\ttotal: 48.1s\tremaining: 4.18s\n",
      "138:\tlearn: 0.0824730\ttest: 0.0825997\tbest: 0.0825997 (138)\ttotal: 48.4s\tremaining: 3.83s\n",
      "139:\tlearn: 0.0824376\ttest: 0.0825659\tbest: 0.0825659 (139)\ttotal: 48.7s\tremaining: 3.48s\n",
      "140:\tlearn: 0.0824129\ttest: 0.0825423\tbest: 0.0825423 (140)\ttotal: 49.1s\tremaining: 3.13s\n",
      "141:\tlearn: 0.0823904\ttest: 0.0825209\tbest: 0.0825209 (141)\ttotal: 49.4s\tremaining: 2.78s\n",
      "142:\tlearn: 0.0823516\ttest: 0.0824867\tbest: 0.0824867 (142)\ttotal: 49.8s\tremaining: 2.44s\n",
      "143:\tlearn: 0.0823345\ttest: 0.0824714\tbest: 0.0824714 (143)\ttotal: 50.1s\tremaining: 2.09s\n",
      "144:\tlearn: 0.0823053\ttest: 0.0824412\tbest: 0.0824412 (144)\ttotal: 50.3s\tremaining: 1.74s\n",
      "145:\tlearn: 0.0822718\ttest: 0.0824093\tbest: 0.0824093 (145)\ttotal: 50.7s\tremaining: 1.39s\n",
      "146:\tlearn: 0.0822416\ttest: 0.0823801\tbest: 0.0823801 (146)\ttotal: 51.1s\tremaining: 1.04s\n",
      "147:\tlearn: 0.0822231\ttest: 0.0823628\tbest: 0.0823628 (147)\ttotal: 51.4s\tremaining: 695ms\n",
      "148:\tlearn: 0.0822058\ttest: 0.0823449\tbest: 0.0823449 (148)\ttotal: 51.7s\tremaining: 347ms\n",
      "149:\tlearn: 0.0821761\ttest: 0.0823173\tbest: 0.0823173 (149)\ttotal: 52s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.08231734946\n",
      "bestIteration = 149\n",
      "\n",
      "Training on fold [2/3]\n",
      "0:\tlearn: 0.5106463\ttest: 0.5108978\tbest: 0.5108978 (0)\ttotal: 407ms\tremaining: 1m\n",
      "1:\tlearn: 0.4629308\ttest: 0.4631490\tbest: 0.4631490 (1)\ttotal: 776ms\tremaining: 57.4s\n",
      "2:\tlearn: 0.4201538\ttest: 0.4203694\tbest: 0.4203694 (2)\ttotal: 1.15s\tremaining: 56.4s\n",
      "3:\tlearn: 0.3819459\ttest: 0.3821522\tbest: 0.3821522 (3)\ttotal: 1.56s\tremaining: 57s\n",
      "4:\tlearn: 0.3478456\ttest: 0.3480353\tbest: 0.3480353 (4)\ttotal: 1.94s\tremaining: 56.4s\n",
      "5:\tlearn: 0.3172850\ttest: 0.3174617\tbest: 0.3174617 (5)\ttotal: 2.33s\tremaining: 55.9s\n",
      "6:\tlearn: 0.2901882\ttest: 0.2903645\tbest: 0.2903645 (6)\ttotal: 2.73s\tremaining: 55.8s\n",
      "7:\tlearn: 0.2659860\ttest: 0.2661464\tbest: 0.2661464 (7)\ttotal: 3.12s\tremaining: 55.4s\n",
      "8:\tlearn: 0.2443663\ttest: 0.2445114\tbest: 0.2445114 (8)\ttotal: 3.5s\tremaining: 54.9s\n",
      "9:\tlearn: 0.2254083\ttest: 0.2255372\tbest: 0.2255372 (9)\ttotal: 3.92s\tremaining: 54.9s\n",
      "10:\tlearn: 0.2084860\ttest: 0.2086157\tbest: 0.2086157 (10)\ttotal: 4.28s\tremaining: 54.1s\n",
      "11:\tlearn: 0.1936423\ttest: 0.1937641\tbest: 0.1937641 (11)\ttotal: 4.66s\tremaining: 53.6s\n",
      "12:\tlearn: 0.1805238\ttest: 0.1806241\tbest: 0.1806241 (12)\ttotal: 5.05s\tremaining: 53.2s\n",
      "13:\tlearn: 0.1689879\ttest: 0.1690824\tbest: 0.1690824 (13)\ttotal: 5.45s\tremaining: 53s\n",
      "14:\tlearn: 0.1588471\ttest: 0.1589374\tbest: 0.1589374 (14)\ttotal: 5.83s\tremaining: 52.5s\n",
      "15:\tlearn: 0.1500269\ttest: 0.1501178\tbest: 0.1501178 (15)\ttotal: 6.21s\tremaining: 52s\n",
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      "132:\tlearn: 0.0828058\ttest: 0.0828738\tbest: 0.0828738 (132)\ttotal: 45.8s\tremaining: 5.86s\n",
      "133:\tlearn: 0.0827682\ttest: 0.0828361\tbest: 0.0828361 (133)\ttotal: 46.1s\tremaining: 5.5s\n",
      "134:\tlearn: 0.0827249\ttest: 0.0827949\tbest: 0.0827949 (134)\ttotal: 46.4s\tremaining: 5.16s\n",
      "135:\tlearn: 0.0826760\ttest: 0.0827504\tbest: 0.0827504 (135)\ttotal: 46.8s\tremaining: 4.81s\n",
      "136:\tlearn: 0.0826452\ttest: 0.0827197\tbest: 0.0827197 (136)\ttotal: 47.1s\tremaining: 4.47s\n",
      "137:\tlearn: 0.0826167\ttest: 0.0826924\tbest: 0.0826924 (137)\ttotal: 47.5s\tremaining: 4.13s\n",
      "138:\tlearn: 0.0825738\ttest: 0.0826513\tbest: 0.0826513 (138)\ttotal: 47.9s\tremaining: 3.79s\n",
      "139:\tlearn: 0.0825482\ttest: 0.0826278\tbest: 0.0826278 (139)\ttotal: 48.1s\tremaining: 3.44s\n",
      "140:\tlearn: 0.0825206\ttest: 0.0826012\tbest: 0.0826012 (140)\ttotal: 48.5s\tremaining: 3.1s\n",
      "141:\tlearn: 0.0824981\ttest: 0.0825811\tbest: 0.0825811 (141)\ttotal: 48.8s\tremaining: 2.75s\n",
      "142:\tlearn: 0.0824596\ttest: 0.0825442\tbest: 0.0825442 (142)\ttotal: 49.1s\tremaining: 2.4s\n",
      "143:\tlearn: 0.0824337\ttest: 0.0825179\tbest: 0.0825179 (143)\ttotal: 49.4s\tremaining: 2.06s\n",
      "144:\tlearn: 0.0824013\ttest: 0.0824885\tbest: 0.0824885 (144)\ttotal: 49.8s\tremaining: 1.72s\n",
      "145:\tlearn: 0.0823734\ttest: 0.0824606\tbest: 0.0824606 (145)\ttotal: 50.1s\tremaining: 1.37s\n",
      "146:\tlearn: 0.0823497\ttest: 0.0824367\tbest: 0.0824367 (146)\ttotal: 50.4s\tremaining: 1.03s\n",
      "147:\tlearn: 0.0823142\ttest: 0.0824053\tbest: 0.0824053 (147)\ttotal: 50.8s\tremaining: 686ms\n",
      "148:\tlearn: 0.0822870\ttest: 0.0823774\tbest: 0.0823774 (148)\ttotal: 51.1s\tremaining: 343ms\n",
      "149:\tlearn: 0.0822602\ttest: 0.0823504\tbest: 0.0823504 (149)\ttotal: 51.6s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.0823503799\n",
      "bestIteration = 149\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'params': {'depth': 8, 'iterations': 150, 'learning_rate': 0.1},\n",
       " 'cv_results': defaultdict(list,\n",
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       "               84,\n",
       "               85,\n",
       "               86,\n",
       "               87,\n",
       "               88,\n",
       "               89,\n",
       "               90,\n",
       "               91,\n",
       "               92,\n",
       "               93,\n",
       "               94,\n",
       "               95,\n",
       "               96,\n",
       "               97,\n",
       "               98,\n",
       "               99,\n",
       "               100,\n",
       "               101,\n",
       "               102,\n",
       "               103,\n",
       "               104,\n",
       "               105,\n",
       "               106,\n",
       "               107,\n",
       "               108,\n",
       "               109,\n",
       "               110,\n",
       "               111,\n",
       "               112,\n",
       "               113,\n",
       "               114,\n",
       "               115,\n",
       "               116,\n",
       "               117,\n",
       "               118,\n",
       "               119,\n",
       "               120,\n",
       "               121,\n",
       "               122,\n",
       "               123,\n",
       "               124,\n",
       "               125,\n",
       "               126,\n",
       "               127,\n",
       "               128,\n",
       "               129,\n",
       "               130,\n",
       "               131,\n",
       "               132,\n",
       "               133,\n",
       "               134,\n",
       "               135,\n",
       "               136,\n",
       "               137,\n",
       "               138,\n",
       "               139,\n",
       "               140,\n",
       "               141,\n",
       "               142,\n",
       "               143,\n",
       "               144,\n",
       "               145,\n",
       "               146,\n",
       "               147,\n",
       "               148,\n",
       "               149],\n",
       "              'test-RMSE-mean': [0.5107205329720088,\n",
       "               0.46299502169944207,\n",
       "               0.4202096601004311,\n",
       "               0.381973141207554,\n",
       "               0.3478441919153113,\n",
       "               0.31729941148039326,\n",
       "               0.2901658257642283,\n",
       "               0.2659945577071592,\n",
       "               0.2444743944352746,\n",
       "               0.22550668137820998,\n",
       "               0.20855619250202462,\n",
       "               0.19373805632889615,\n",
       "               0.18060818872535925,\n",
       "               0.16907683053847192,\n",
       "               0.15897726495236145,\n",
       "               0.15021779176826386,\n",
       "               0.14255398448594517,\n",
       "               0.135852958664149,\n",
       "               0.1300503164233084,\n",
       "               0.12512871787698857,\n",
       "               0.12075842724123108,\n",
       "               0.11712250533914108,\n",
       "               0.11407822445341272,\n",
       "               0.11125949535150366,\n",
       "               0.10896935322608718,\n",
       "               0.10686453182771698,\n",
       "               0.1051820512965254,\n",
       "               0.10369310065167653,\n",
       "               0.10237809487924432,\n",
       "               0.1011994460645159,\n",
       "               0.10006842146275165,\n",
       "               0.09911068640933636,\n",
       "               0.09816539247000772,\n",
       "               0.09731223761814378,\n",
       "               0.09658496159349879,\n",
       "               0.09589547198396269,\n",
       "               0.0952250899127798,\n",
       "               0.09472083833620197,\n",
       "               0.09425855309740612,\n",
       "               0.09385545703785791,\n",
       "               0.0934103886079858,\n",
       "               0.09295012007811565,\n",
       "               0.09261090971873925,\n",
       "               0.09222290163620071,\n",
       "               0.0919472224554994,\n",
       "               0.09159831299655703,\n",
       "               0.09132377486401588,\n",
       "               0.09107696222836269,\n",
       "               0.09082547703070436,\n",
       "               0.09059801918333572,\n",
       "               0.090401528171223,\n",
       "               0.09014238509107096,\n",
       "               0.08993026675109726,\n",
       "               0.08975090286431757,\n",
       "               0.08948806896842816,\n",
       "               0.0891894263512068,\n",
       "               0.08901618365106016,\n",
       "               0.08887215060880842,\n",
       "               0.08868737165982991,\n",
       "               0.08852559866721527,\n",
       "               0.08838772612825437,\n",
       "               0.08824131972449738,\n",
       "               0.08806659563402423,\n",
       "               0.0879485785048502,\n",
       "               0.08779893905357115,\n",
       "               0.08761283246241615,\n",
       "               0.08742964353308962,\n",
       "               0.08728795589299217,\n",
       "               0.08716337075728718,\n",
       "               0.08705262918638972,\n",
       "               0.08689128371863615,\n",
       "               0.0867666150084956,\n",
       "               0.08668947922670217,\n",
       "               0.08659649618092473,\n",
       "               0.08649806070526328,\n",
       "               0.0863909066122402,\n",
       "               0.08628281070243855,\n",
       "               0.0861818793124834,\n",
       "               0.08604579320338725,\n",
       "               0.0859516322409525,\n",
       "               0.0858461075215069,\n",
       "               0.08574297930465354,\n",
       "               0.08564267695220514,\n",
       "               0.08553012208494265,\n",
       "               0.08543769868946278,\n",
       "               0.0853586676100967,\n",
       "               0.08527600661530686,\n",
       "               0.08517103927783197,\n",
       "               0.08510406871605836,\n",
       "               0.08501159123160155,\n",
       "               0.08495772616409886,\n",
       "               0.08487395492149719,\n",
       "               0.08479139822307631,\n",
       "               0.08472232031498933,\n",
       "               0.08466278383804225,\n",
       "               0.08461935203028172,\n",
       "               0.084555131581201,\n",
       "               0.08448803000237215,\n",
       "               0.08441948406225723,\n",
       "               0.0843573165807534,\n",
       "               0.08429950577796852,\n",
       "               0.08424455142846077,\n",
       "               0.0842019032997691,\n",
       "               0.0841539703425529,\n",
       "               0.08407033360870379,\n",
       "               0.08402225463434398,\n",
       "               0.0839592731566924,\n",
       "               0.08391598563410729,\n",
       "               0.08386237690228299,\n",
       "               0.08380137220566723,\n",
       "               0.08374022621187742,\n",
       "               0.0836918761818638,\n",
       "               0.08365682985624491,\n",
       "               0.08361780881144358,\n",
       "               0.08356591310662904,\n",
       "               0.08353014894316363,\n",
       "               0.08347755048616667,\n",
       "               0.08342638567775033,\n",
       "               0.08338835133283536,\n",
       "               0.08335773104530143,\n",
       "               0.08330742023555808,\n",
       "               0.08326645640467062,\n",
       "               0.08322710507055738,\n",
       "               0.08319057025728868,\n",
       "               0.08315050132884133,\n",
       "               0.08311120336717903,\n",
       "               0.08307727335160771,\n",
       "               0.08304599371349387,\n",
       "               0.08302077647347282,\n",
       "               0.08298868014022762,\n",
       "               0.08296125528963899,\n",
       "               0.08292208776136374,\n",
       "               0.08289735430526117,\n",
       "               0.08286192243006517,\n",
       "               0.08282047936181451,\n",
       "               0.08277746776239048,\n",
       "               0.08274301913575169,\n",
       "               0.08270813599537548,\n",
       "               0.0826743515359457,\n",
       "               0.08264676578873004,\n",
       "               0.08262325898240509,\n",
       "               0.08260297254640875,\n",
       "               0.08257300396562414,\n",
       "               0.08254913885063254,\n",
       "               0.08251819040491404,\n",
       "               0.08248606402425519,\n",
       "               0.08245681958331802,\n",
       "               0.08243050285294932,\n",
       "               0.08240724048452557,\n",
       "               0.08238320610870208],\n",
       "              'test-RMSE-std': [0.00021174491529256712,\n",
       "               0.00020623907001023325,\n",
       "               0.00020558625149389096,\n",
       "               0.00021141860159933903,\n",
       "               0.00022004227878844352,\n",
       "               0.00021696132328953983,\n",
       "               0.00023298660004213997,\n",
       "               0.00016850948896114808,\n",
       "               3.2152951062011365e-05,\n",
       "               2.7123861057631013e-05,\n",
       "               5.5391778903530106e-05,\n",
       "               3.901509014951496e-05,\n",
       "               1.965712406602358e-05,\n",
       "               5.090912093145568e-05,\n",
       "               3.631159853867736e-05,\n",
       "               0.00010797663863604003,\n",
       "               0.00011515118284795924,\n",
       "               8.37741711783697e-05,\n",
       "               0.0002665177988110158,\n",
       "               0.0002814146441749166,\n",
       "               0.00019501477602725347,\n",
       "               0.0002019964584130891,\n",
       "               0.00022473140912729312,\n",
       "               0.0002643883879169559,\n",
       "               0.0002382178168282105,\n",
       "               0.00028193561947753945,\n",
       "               0.00024409076002566057,\n",
       "               0.0002388375765869828,\n",
       "               0.00029713526424081214,\n",
       "               0.00036492466970341716,\n",
       "               0.00019374727627923392,\n",
       "               0.00021577557885478426,\n",
       "               0.00019573572104172266,\n",
       "               0.00036470329635815733,\n",
       "               0.0003123998598495732,\n",
       "               0.00024084135252236467,\n",
       "               0.00035684621524755824,\n",
       "               0.0002934519238685843,\n",
       "               0.0002659374928393007,\n",
       "               0.00019986024905587887,\n",
       "               0.00017468071074570817,\n",
       "               0.00016316899124153625,\n",
       "               0.0002199263026796614,\n",
       "               0.00023590545779804176,\n",
       "               0.00020134659162468537,\n",
       "               0.0002895778411908492,\n",
       "               0.000319177183644729,\n",
       "               0.0002848563818896142,\n",
       "               0.00027165722679597,\n",
       "               0.00028943711458258927,\n",
       "               0.0003161824422306208,\n",
       "               0.0002922926947733988,\n",
       "               0.0002833493047998368,\n",
       "               0.0002893597645186426,\n",
       "               0.0001929307202160656,\n",
       "               0.00018312255070555803,\n",
       "               0.0001803381749263895,\n",
       "               0.00015733149043307208,\n",
       "               0.00011499661475610729,\n",
       "               0.00012166926296736303,\n",
       "               0.00012686302481120667,\n",
       "               0.0001537181574644047,\n",
       "               0.00014276618338937138,\n",
       "               0.0001419021253974873,\n",
       "               0.00016250542344192862,\n",
       "               0.00012164250300872838,\n",
       "               0.00015532928033273497,\n",
       "               0.00015749182508428825,\n",
       "               0.0001257438447307436,\n",
       "               9.059754146731962e-05,\n",
       "               6.259824482538176e-05,\n",
       "               3.957403172145993e-05,\n",
       "               3.4334435644109085e-05,\n",
       "               3.94155540220611e-05,\n",
       "               2.3557848357660917e-05,\n",
       "               7.278133862040607e-05,\n",
       "               0.00013428596116200393,\n",
       "               0.00014765199858098298,\n",
       "               0.00011629742988322212,\n",
       "               0.0001251183632606862,\n",
       "               0.00010326807163522064,\n",
       "               8.999551840116676e-05,\n",
       "               8.094979569286269e-05,\n",
       "               8.896437214554294e-05,\n",
       "               0.00012423949311006293,\n",
       "               0.00013670323774901056,\n",
       "               0.0001193207479908847,\n",
       "               9.272022732434673e-05,\n",
       "               8.012106593616933e-05,\n",
       "               0.00010693061345991589,\n",
       "               0.00010211642926427565,\n",
       "               0.00012211778553032136,\n",
       "               0.00015606919863512975,\n",
       "               0.0001393257442240693,\n",
       "               0.0001484948285754064,\n",
       "               0.00014443249658496518,\n",
       "               0.00014913839809850606,\n",
       "               0.00016604293594747662,\n",
       "               0.0001615056337148852,\n",
       "               0.00014035783383088197,\n",
       "               0.000126768606966811,\n",
       "               9.559039550261529e-05,\n",
       "               9.71191692705984e-05,\n",
       "               9.288215430461441e-05,\n",
       "               9.093250520669968e-05,\n",
       "               9.446163111616736e-05,\n",
       "               9.041095588734323e-05,\n",
       "               8.79511356005047e-05,\n",
       "               8.40339770356144e-05,\n",
       "               9.559497662351742e-05,\n",
       "               8.51477260938073e-05,\n",
       "               0.00010095620209365893,\n",
       "               9.422227964512336e-05,\n",
       "               9.605927027654272e-05,\n",
       "               8.490102046685615e-05,\n",
       "               8.196744693042454e-05,\n",
       "               7.967866360365672e-05,\n",
       "               6.235832002901535e-05,\n",
       "               5.7338479413947924e-05,\n",
       "               6.073515288079342e-05,\n",
       "               5.9504637788575125e-05,\n",
       "               6.24311324684733e-05,\n",
       "               6.625574042489977e-05,\n",
       "               5.8572329503176935e-05,\n",
       "               5.5246599390900126e-05,\n",
       "               6.82126617362577e-05,\n",
       "               8.133300045272282e-05,\n",
       "               8.90022926711112e-05,\n",
       "               8.777746196653613e-05,\n",
       "               9.749630486618798e-05,\n",
       "               9.689688448086643e-05,\n",
       "               0.00010252230596982504,\n",
       "               0.00010279927629631515,\n",
       "               0.00010049702955089683,\n",
       "               9.083534462044422e-05,\n",
       "               8.91327301948619e-05,\n",
       "               8.781109530387963e-05,\n",
       "               8.947119113892455e-05,\n",
       "               8.845790544141723e-05,\n",
       "               9.179355534467657e-05,\n",
       "               9.39337781613613e-05,\n",
       "               9.493576907710334e-05,\n",
       "               0.00010369428491762531,\n",
       "               9.7157054912165e-05,\n",
       "               9.532491621483917e-05,\n",
       "               9.220961620186264e-05,\n",
       "               8.850841751091987e-05,\n",
       "               8.322792674016682e-05,\n",
       "               8.145309325245966e-05,\n",
       "               8.704299403203181e-05],\n",
       "              'train-RMSE-mean': [0.5107195488080026,\n",
       "               0.4629937722425423,\n",
       "               0.4202081239618429,\n",
       "               0.38197173009006913,\n",
       "               0.34784128768539485,\n",
       "               0.3172989749205455,\n",
       "               0.2901630116580962,\n",
       "               0.26599423299311703,\n",
       "               0.24447604286805025,\n",
       "               0.22550658692704964,\n",
       "               0.20855188289913953,\n",
       "               0.19372965255861532,\n",
       "               0.1806028478347138,\n",
       "               0.16907150797626427,\n",
       "               0.15897254130686886,\n",
       "               0.15020520944472704,\n",
       "               0.1425434013054454,\n",
       "               0.13584174508889624,\n",
       "               0.1300345255934462,\n",
       "               0.12511276152681966,\n",
       "               0.12074321608079182,\n",
       "               0.11710732123680868,\n",
       "               0.11405940648105413,\n",
       "               0.11124185529252507,\n",
       "               0.10895185610846157,\n",
       "               0.10684901257305957,\n",
       "               0.10516130329854072,\n",
       "               0.1036719836792051,\n",
       "               0.10235361501621075,\n",
       "               0.10117191641287643,\n",
       "               0.10004222785339024,\n",
       "               0.09908574019349863,\n",
       "               0.09813765377511119,\n",
       "               0.09728238630561627,\n",
       "               0.09655360946409264,\n",
       "               0.09586386910503097,\n",
       "               0.0951922751814096,\n",
       "               0.09468475799801372,\n",
       "               0.09422160397160544,\n",
       "               0.09381945010796604,\n",
       "               0.09337404650354915,\n",
       "               0.09291212879376747,\n",
       "               0.09257005196160221,\n",
       "               0.09218097407245628,\n",
       "               0.09190256729038804,\n",
       "               0.09154977540330395,\n",
       "               0.09127398746607686,\n",
       "               0.09102600009866434,\n",
       "               0.09077580266230052,\n",
       "               0.09054865889433056,\n",
       "               0.09035049415513752,\n",
       "               0.09008854051931164,\n",
       "               0.08987423907837915,\n",
       "               0.08969476978844015,\n",
       "               0.08943191645697939,\n",
       "               0.08913063266636755,\n",
       "               0.08895768492019612,\n",
       "               0.08881196889589688,\n",
       "               0.0886263665392637,\n",
       "               0.08846474799651043,\n",
       "               0.08832276403252363,\n",
       "               0.08817677490308318,\n",
       "               0.08800165393363184,\n",
       "               0.08788190599910634,\n",
       "               0.08773201552446125,\n",
       "               0.08754519733375117,\n",
       "               0.08736113639999037,\n",
       "               0.08721772380430959,\n",
       "               0.08709328209650043,\n",
       "               0.08698217142046309,\n",
       "               0.08681801800971649,\n",
       "               0.08669052097914642,\n",
       "               0.08661288712381503,\n",
       "               0.08651889128939505,\n",
       "               0.08641943744634924,\n",
       "               0.08631180285935587,\n",
       "               0.08620295948725043,\n",
       "               0.08610207643382783,\n",
       "               0.08596315828068612,\n",
       "               0.08586860989627515,\n",
       "               0.08576112647799654,\n",
       "               0.08565636170754631,\n",
       "               0.08555607073525483,\n",
       "               0.08544483197440171,\n",
       "               0.08534894573818676,\n",
       "               0.08526922507821823,\n",
       "               0.08518552288623131,\n",
       "               0.08508015608962628,\n",
       "               0.08501263494761348,\n",
       "               0.08491993524392287,\n",
       "               0.08486540990286463,\n",
       "               0.08478049612488874,\n",
       "               0.08469796175791595,\n",
       "               0.08462812233050465,\n",
       "               0.08456776798850059,\n",
       "               0.08452226130378428,\n",
       "               0.08445809812283463,\n",
       "               0.08439049018684626,\n",
       "               0.08431952261810578,\n",
       "               0.08425479869562498,\n",
       "               0.08419465985890917,\n",
       "               0.08413913778729777,\n",
       "               0.08409742947190661,\n",
       "               0.08404793212490548,\n",
       "               0.08396434688556755,\n",
       "               0.08391286304596457,\n",
       "               0.08384921427910569,\n",
       "               0.08380521143162752,\n",
       "               0.08374977027382008,\n",
       "               0.08368744243236327,\n",
       "               0.08362517686492825,\n",
       "               0.08357693087564126,\n",
       "               0.08354064107335639,\n",
       "               0.08349876084647771,\n",
       "               0.0834451043963879,\n",
       "               0.08340773422822338,\n",
       "               0.08335295238189576,\n",
       "               0.08330111173252047,\n",
       "               0.08326314636335903,\n",
       "               0.08323190891116004,\n",
       "               0.08318030897573657,\n",
       "               0.08313784227654492,\n",
       "               0.08309792156532857,\n",
       "               0.08306040410530764,\n",
       "               0.08301837173801735,\n",
       "               0.08297728609488214,\n",
       "               0.08294208633279819,\n",
       "               0.0829100702181008,\n",
       "               0.08288341739101328,\n",
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       "              'train-RMSE-std': [8.856287337884406e-05,\n",
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       "               4.114688051751039e-05,\n",
       "               4.2057759780873125e-05]})}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid={'iterations':[100, 150],\n",
    "      'learning_rate':[0.03,0.1],\n",
    "      'depth':[2 ,4, 6, 8]}\n",
    "\n",
    "model.grid_search(grid, train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "21dd1357",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T15:04:44.298803Z",
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "feature_importance_df=pd.DataFrame()\n",
    "feature_importance_df['features']=features\n",
    "feature_importance_df['importance']=model.feature_importances_\n",
    "\n",
    "feature_importance_df=feature_importance_df.sort_values(by=['importance'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "70ad1222",
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    },
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "    }\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>killPlace</td>\n",
       "      <td>40.989540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>totalDistance</td>\n",
       "      <td>14.135225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>travelDistance</td>\n",
       "      <td>10.442443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>killsNorm</td>\n",
       "      <td>9.675370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>killStreaks</td>\n",
       "      <td>6.892138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>matchDurationNorm</td>\n",
       "      <td>5.365726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PlayersJoined</td>\n",
       "      <td>4.459535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>numGroups</td>\n",
       "      <td>2.115840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>healsnboost</td>\n",
       "      <td>1.791765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DBNOs</td>\n",
       "      <td>1.040025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>weaponsAcquired</td>\n",
       "      <td>0.712617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>maxPlaceNorm</td>\n",
       "      <td>0.554967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>matchType_squad</td>\n",
       "      <td>0.421833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>assist</td>\n",
       "      <td>0.410710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>longestKill</td>\n",
       "      <td>0.353922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>matchType_solo-fpp</td>\n",
       "      <td>0.113378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>rankPoints</td>\n",
       "      <td>0.094217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>matchType_solo</td>\n",
       "      <td>0.076811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>winPoints</td>\n",
       "      <td>0.059496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>matchType_duo-fpp</td>\n",
       "      <td>0.059082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>teamKills</td>\n",
       "      <td>0.053351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>damageDealtNorm</td>\n",
       "      <td>0.049218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>roadKills</td>\n",
       "      <td>0.035442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>matchType_duo</td>\n",
       "      <td>0.030458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>matchType_crashfpp</td>\n",
       "      <td>0.025136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>matchType_squad-fpp</td>\n",
       "      <td>0.023053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>matchType_normal-squad-fpp</td>\n",
       "      <td>0.011384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>killPoints</td>\n",
       "      <td>0.007319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>matchType_normal-solo</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>matchType_normal-squad</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>matchType_normal-solo-fpp</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>vehicleDestroys</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>matchType_normal-duo-fpp</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>matchType_normal-duo</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>matchType_flaretpp</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>matchType_flarefpp</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>matchType_crashtpp</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>headshotKills</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Headshot_rate</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>KillsWithoutMoving_False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      features  importance\n",
       "2                    killPlace   40.989540\n",
       "14               totalDistance   14.135225\n",
       "20              travelDistance   10.442443\n",
       "16                   killsNorm    9.675370\n",
       "4                  killStreaks    6.892138\n",
       "19           matchDurationNorm    5.365726\n",
       "13               PlayersJoined    4.459535\n",
       "6                    numGroups    2.115840\n",
       "21                 healsnboost    1.791765\n",
       "0                        DBNOs    1.040025\n",
       "11             weaponsAcquired    0.712617\n",
       "18                maxPlaceNorm    0.554967\n",
       "37             matchType_squad    0.421833\n",
       "22                      assist    0.410710\n",
       "5                  longestKill    0.353922\n",
       "36          matchType_solo-fpp    0.113378\n",
       "7                   rankPoints    0.094217\n",
       "35              matchType_solo    0.076811\n",
       "12                   winPoints    0.059496\n",
       "26           matchType_duo-fpp    0.059082\n",
       "9                    teamKills    0.053351\n",
       "17             damageDealtNorm    0.049218\n",
       "8                    roadKills    0.035442\n",
       "25               matchType_duo    0.030458\n",
       "23          matchType_crashfpp    0.025136\n",
       "38         matchType_squad-fpp    0.023053\n",
       "34  matchType_normal-squad-fpp    0.011384\n",
       "3                   killPoints    0.007319\n",
       "31       matchType_normal-solo    0.000000\n",
       "33      matchType_normal-squad    0.000000\n",
       "32   matchType_normal-solo-fpp    0.000000\n",
       "10             vehicleDestroys    0.000000\n",
       "30    matchType_normal-duo-fpp    0.000000\n",
       "29        matchType_normal-duo    0.000000\n",
       "28          matchType_flaretpp    0.000000\n",
       "27          matchType_flarefpp    0.000000\n",
       "24          matchType_crashtpp    0.000000\n",
       "1                headshotKills    0.000000\n",
       "15               Headshot_rate    0.000000\n",
       "39    KillsWithoutMoving_False    0.000000"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance_df"
   ]
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     "start_time": "2024-06-24T15:04:44.825066",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(feature_importance_df.features,feature_importance_df.importance,color='turquoise')\n",
    "plt.ylabel(\"CatBoost Feature Importance\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "42e9aaf1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T15:04:45.897776Z",
     "iopub.status.busy": "2024-06-24T15:04:45.897363Z",
     "iopub.status.idle": "2024-06-24T15:04:46.281777Z",
     "shell.execute_reply": "2024-06-24T15:04:46.280615Z"
    },
    "papermill": {
     "duration": 0.557833,
     "end_time": "2024-06-24T15:04:46.284506",
     "exception": false,
     "start_time": "2024-06-24T15:04:45.726673",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pred=model.predict(xtest)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c4a95009",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-24T15:04:46.687079Z",
     "iopub.status.busy": "2024-06-24T15:04:46.686692Z",
     "iopub.status.idle": "2024-06-24T15:04:46.717468Z",
     "shell.execute_reply": "2024-06-24T15:04:46.716173Z"
    },
    "papermill": {
     "duration": 0.267516,
     "end_time": "2024-06-24T15:04:46.719825",
     "exception": false,
     "start_time": "2024-06-24T15:04:46.452309",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TEsting Performance\n",
      "RMSE: 0.08\n",
      "R20.93\n"
     ]
    }
   ],
   "source": [
    "rmse=np.sqrt(mean_squared_error(ytest,pred))\n",
    "r2=r2_score(ytest,pred)\n",
    "\n",
    "print(\"TEsting Performance\")\n",
    "\n",
    "print(\"RMSE: {:.2f}\".format(rmse))\n",
    "print(\"R2{:.2f}\".format(r2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d41ec341",
   "metadata": {
    "papermill": {
     "duration": 0.16825,
     "end_time": "2024-06-24T15:04:47.053024",
     "exception": false,
     "start_time": "2024-06-24T15:04:46.884774",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Rmse is 0.08 which says that our model prediction is only 8 % wrong  whhich is good\n",
    "# R2 is 0.93 very much close to 1 \n",
    "# Model performance is good and it is not overfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec9d1245",
   "metadata": {
    "papermill": {
     "duration": 0.164651,
     "end_time": "2024-06-24T15:04:47.383089",
     "exception": false,
     "start_time": "2024-06-24T15:04:47.218438",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<center><img src  =\"https://media.giphy.com/media/KB89dMAtH79VIvxNCW/giphy.gif\"></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df4e74a5",
   "metadata": {
    "papermill": {
     "duration": 0.165601,
     "end_time": "2024-06-24T15:04:47.714468",
     "exception": false,
     "start_time": "2024-06-24T15:04:47.548867",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7dae005",
   "metadata": {
    "papermill": {
     "duration": 0.16699,
     "end_time": "2024-06-24T15:04:48.046614",
     "exception": false,
     "start_time": "2024-06-24T15:04:47.879624",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "datasetId": 2634885,
     "sourceId": 4508299,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30558,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 1004.747157,
   "end_time": "2024-06-24T15:04:49.739962",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-06-24T14:48:04.992805",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}