README.md

py-decision-tree

Simple Python Implementation of a Decision Tree with Numpy.

PyDecisionTree

PyDecisionTree is a simple Python Framework for using Decision Trees (DTs). The decision tree is based on the Continuous and Categorical Trees (CART) algorithm. Currently, the implementation cannot handle categorical data well, because of NumPys implementation of arrays with multiple dtypes!

Using DTs with PyDecisionTree

For the following we want to train a DT Classifier for the iris dataset:

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

random_state = 0

# Load the iris dataset
X, y = load_iris(return_X_y=True)

# Split data into train and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True, random_state=random_state)

First we initialize our DTs model:

from PyDecisionTree.model import DecisionTreeClassifier

# Initialize the decision tree classifier
model = DecisionTreeClassifier(random_state=random_state)

Now we have to use the .fit() method to build the decision tree:

# Train the model
model.fit(X_train, y_train)

After fitting the model we can now use the DT to make inferences on new unseen data points:

# Predict new values with the test dataset
y_pred = model.predict(X_test)

Future Features

The following list defines features, that are currently on work:

• Implement a better visualization tool for the trained decision tree
• Adjust the code to also handle categorical features
• Implement the log loss as an splitter option for decision tree classifier
• Adjust workflow file to test on windows & linux