{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"Transforms categorical variables into numeric representations using scikit-learn-compatible encoders, supporting both unsupervised methods (one-hot, binary, ordinal) and supervised techniques (target encoding, CatBoost encoding).","skillfed_tags":["feature-engineering","categorical-data","sklearn-compatible"],"use_cases":["Encode categorical features like gender or country in a classification pipeline using one-hot or binary encoding","Apply target encoding to high-cardinality categorical variables to capture predictive signal from the target variable","Use LeaveOneOut encoding in supervised settings to prevent overfitting while maintaining target information","Build feature engineering pipelines that automatically encode all object-type columns in a pandas dataframe","Compare multiple encoding strategies on the same dataset to select the best performing method"],"what_it_does":"Category-encoders is a scikit-learn-compatible library that converts categorical variables into numeric form using a range of encoding strategies. It provides both unsupervised methods (binary, one-hot, ordinal, hashing) that treat all categories equally, and supervised methods (target encoding, CatBoost encoding, leave-one-out) that leverage target information to create more predictive numeric representations. The library integrates seamlessly into sklearn pipelines and accepts numpy arrays or pandas dataframes as input.\n\nThe package is designed for machine learning workflows where categorical features must be converted to numeric form. It handles the common problem of deciding which encoding method to use by offering multiple techniques with different statistical properties and computational trade-offs. Supervised encoders can reduce overfitting through nested cross-validation wrappers, and the library supports both classification and regression tasks through specialized wrappers.","worth_installing":"Yes. The package is actively maintained, has low installation friction, carries no known vulnerabilities, and provides a well-documented suite of encoding methods that integrate directly into sklearn workflows. It solves a common data preprocessing problem with multiple proven techniques. Install it if you work with categorical data in machine learning projects."},"id":"category-encoders","links":{"html":"https://skillfed.io/packages/category-encoders","md":"https://skillfed.io/packages/category-encoders.md","pypi":"https://pypi.org/project/category-encoders/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-26","license_spdx":null,"license_treatment":"permissive","name":"category-encoders","python_support":"supports_current","summary":"A package for encoding categorical variables for machine learning"},"popularity":{"monthly_downloads":3123199,"position":2741,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.10.0"}