CatBoost
CatBoost is a machine learning algorithm that uses gradient boosting on decision trees. It is available as an open source library.
Model analysis
Metrics
Visualization tools
Exporting models
Educational materials
- Installation
- Overview
- Python package installation
- CatBoost for Apache Spark installation
- R package installation
- Command-line version binary
- Key Features
- Training
- Training on GPU
- Regular prediction
- Staged prediction
- Cross-validation
- Feature importances
- User-defined metrics
- Using the overfitting detector
- Export a model to CoreML
- Pre-trained data
- Calculate metrics
- Categorical features
- Text features
- Embeddings features
- Implemented metrics
- Export a model to Python or C++
- Export a model to JSON
- Object importances
- Training parameters
- Python package
- Quick start
- CatBoost
- Overview
- fit
- predict
- Attributes
- calc_leaf_indexes
- calc_feature_statistics
- compare
- copy
- eval_metrics
- get_all_params
- get_best_iteration
- get_best_score
- get_borders
- get_evals_result
- get_feature_importance
- get_metadata
- get_object_importance
- get_param
- get_params
- get_scale_and_bias
- get_test_eval
- grid_search
- is_fitted
- load_model
- plot_predictions
- plot_tree
- randomized_search
- save_model
- save_borders
- select_features
- set_scale_and_bias
- set_feature_names
- set_params
- shrink
- staged_predict
- virtual_ensembles_predict
- CatBoostClassifier
- Overview
- fit
- predict
- predict_proba
- Attributes
- calc_leaf_indexes
- calc_feature_statistics
- compare
- copy
- eval_metrics
- get_all_params
- get_best_iteration
- get_best_score
- get_borders
- get_evals_result
- get_feature_importance
- get_metadata
- get_object_importance
- get_param
- get_params
- get_probability_threshold
- get_scale_and_bias
- get_test_eval
- grid_search
- is_fitted
- load_model
- plot_predictions
- plot_tree
- randomized_search
- save_borders
- save_model
- score
- select_features
- set_feature_names
- set_params
- set_probability_threshold
- set_scale_and_bias
- shrink
- staged_predict
- staged_predict_proba
- CatBoostRanker
- Overview
- fit
- predict
- Attributes
- calc_leaf_indexes
- calc_feature_statistics
- compare
- copy
- eval_metrics
- get_all_params
- get_best_iteration
- get_best_score
- get_borders
- get_evals_result
- get_feature_importance
- get_metadata
- get_object_importance
- get_param
- get_params
- get_scale_and_bias
- get_test_eval
- grid_search
- is_fitted
- load_model
- plot_predictions
- plot_tree
- randomized_search
- save_model
- save_borders
- score
- select_features
- set_scale_and_bias
- set_feature_names
- set_params
- shrink
- staged_predict
- virtual_ensembles_predict
- CatBoostRegressor
- Overview
- fit
- predict
- Attributes
- calc_leaf_indexes
- calc_feature_statistics
- copy
- compare
- eval_metrics
- get_all_params
- get_best_iteration
- get_best_score
- get_borders
- get_evals_result
- get_feature_importance
- get_metadata
- get_object_importance
- get_param
- get_params
- get_scale_and_bias
- get_test_eval
- grid_search
- is_fitted
- load_model
- plot_predictions
- plot_tree
- randomized_search
- save_borders
- save_model
- score
- select_features
- set_feature_names
- set_params
- set_scale_and_bias
- shrink
- staged_predict
- cv
- datasets
- FeaturesData
- MetricVisualizer
- Pool
- Overview
- Attributes
- get_baseline
- get_cat_feature_indices
- get_embedding_feature_indices
- get_features
- get_group_id
- get_label
- get_text_feature_indices
- get_weight
- is_quantized
- num_col
- num_row
- quantize
- save
- save_quantization_borders
- set_baseline
- set_feature_names
- set_group_id
- set_group_weight
- set_pairs
- set_pairs_weight
- set_subgroup_id
- set_timestamp
- set_weight
- slice
- Pool initialization
- sum_models
- to_classifier
- to_regressor
- train
- Text processing
- utils
- Usage examples
- CatBoost for Apache Spark
- Overview
- Quick start
- Spark cluster configuration
- API documentation
- Known limitations
- Usage examples
- R package
- Command-line version
- Applying models
- Objectives and metrics
- Model analysis
- Data format description
- Parameter tuning
- Speeding up the training
- Data visualization
- Algorithm details
- FAQ
- Educational materials
- Development and contributions
- Contacts