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
- CatBoost Encoder
- Count Encoder
- Generalized Linear Mixed Model Encoder
- Helmert Coding
- James-Stein Encoder
- Leave One Out
- One Hot
- Polynomial Coding
- Quantile Encoder
- Sum Coding
- Summary Encoder
- Target Encoder
- Weight of Evidence