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CATBOOST
Gradient boosting with native categorical feature support
Apache-2.0
ABOUT
Gradient boosting models often require extensive preprocessing to handle categorical features, missing values, and hyperparameter tuning, adding complexity to ML pipelines. CatBoost provides a gradient boosting library that natively handles categorical features without manual encoding, delivers state-of-the-art results with default parameters, and supports both CPU and GPU training—reducing pipeline complexity while maintaining high prediction quality.
INSTALL
pip install catboostINTEGRATION GUIDE
1. Train ML models on datasets with many categorical features without manual encoding or preprocessing
2. Build high-performance classification and regression models with minimal hyperparameter tuning using default parameters
3. Run gradient boosting on GPU for faster training on large datasets with built-in cross-validation
4. Deploy production ML pipelines with automatic handling of missing values, text features, and imbalanced data
TAGS
gradient-boostingmachine-learningclassificationregressionrankinggpupythoncategorical-data