Overview of CatBoost

CatBoost is a machine learning algorithm that uses gradient boosting on decision trees. It is available as an open source library.


Key features
	Training Training Training on GPU Python train function Cross-validation Overfitting detector Pre-trained data Categorical features		Applying models Regular prediction Staged prediction С and C++ API Calculate metrics Applying the model in ClickHouse
	Model analysis Feature importances Object importances		Metrics Implemented metrics User-defined metrics
	Recovery Snapshots		Visualization tools Jupyter Notebook CatBoost Viewer TensorBoard
	Exporting models Export a model to CoreML Export a model to Python or C++ Export a model to JSON		Educational materials Tutorials Reference papers Videos


Key features
	Training Training Training on GPU Python train function Cross-validation Overfitting detector Pre-trained data Categorical features		Applying models Regular prediction Staged prediction С and C++ API Calculate metrics Applying the model in ClickHouse
	Model analysis Feature importances Object importances		Metrics Implemented metrics User-defined metrics
	Recovery Snapshots		Visualization tools Jupyter Notebook CatBoost Viewer TensorBoard
	Exporting models Export a model to CoreML Export a model to Python or C++ Export a model to JSON		Educational materials Tutorials Reference papers Videos