A set of scikit-learn-style transformers for encoding categorical variables into numeric with different techniques.

Category Encoders is a Python library for encoding categorical variables for machine learning tasks. It is available on contrib.scikit-learn.org and extends the capabilities of scikit-learn’s preprocessing module.

The library provides several powerful encoding techniques for dealing with categorical data, including:

• Ordinal encoding: maps categorical variables to integer values based on their order of appearance
• One-hot encoding: creates a binary feature for each category in a variable
• Binary encoding: maps each category to a binary code
• Target encoding: encodes each category with the mean target value for that category
• Hashing encoding: maps each category to a random index in a hash table

Category Encoders also supports a range of advanced features, such as handling missing values, combining multiple encoders, and applying encoders to specific subsets of features.

Overall, Category Encoders is a useful tool for preprocessing categorical data and improving the accuracy and performance of machine learning models.

• Backward Difference Coding
• BaseN
• Binary
• CatBoost Encoder
• Count Encoder
• Generalized Linear Mixed Model Encoder
• Gray
• Hashing
• Helmert Coding
• James-Stein Encoder
• Leave One Out
• M-estimate
• One Hot
• Ordinal
• Polynomial Coding
• Quantile Encoder
• Sum Coding
• Summary Encoder
• Target Encoder
• Weight of Evidence
• Wrappers