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 Text features		Applying models Regular prediction С and C++ Java Rust Calculate metrics Staged prediction Applying the model in ClickHouse
	Model analysis Feature importances Object importances		Metrics Implemented metrics User-defined metrics
	Recovery Snapshots		Visualization tools Jupyter Notebook TensorBoard
	Exporting models CoreML Python or C++ JSON ONNX PMML		Educational materials Tutorials Reference papers Videos

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