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CatBoost Encoder is a feature encoding method used in machine learning to convert categorical features into numerical features that can be used in machine learning algorithms. Itis a part of the CatBoost library, which is a popular gradient boosting framework.
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The CatBoost Encoder works by assigning numerical values to each category in a feature based on its relationship with the target variable. This encoding method can handle high-cardinality categorical features with many unique categories.
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The CatBoost Encoder uses two types of encoding techniques: Target Encoding and Count Encoding. Target Encoding replaces each category with the average value of the target variable for that category. Count Encoding replaces each category with the number of times it appears in the data set.
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One of the advantages of using CatBoost Encoder is that it can capture the non-linear relationship between categorical features and the target variable. It can also handle missing values in categorical features and can be used with both classification and regression problems.
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Overall, CatBoost Encoder is a useful tool for preprocessing categorical features in machine learning models and can improve the performance of the models when compared to other encoding methods.
!pip install category_encodersHappy Encoding