Contents
Overview of CatBoost
Installation
Python package
R package
Command-line version
Applying models
Objectives and metrics
Model analysis
Data format description
Parameter tuning
Speeding up the training
Data visualization
FAQ
Educational materials
Development and contributions
Algorithm details
Contacts
Quick start
Training parameters
CatBoost
CatBoostClassifier
CatBoostRegressor
cv
datasets
FeaturesData
MetricVisualizer
Pool
sum_models
to_classifier
to_regressor
train
Text processing
utils
Usage examples
Text processing
Tokenizer
Dictionary
Dictionary
fit
apply
size
get_token
get_tokens
get_top_tokens
unknown_token_id
end_of_sentence_token_id
min_unused_token_id
load
save
Overview of CatBoost
Installation
Python package installation
pip install
conda install
Build from source on Linux and macOS
Build from source on Windows
Build a wheel package
Additional packages for data visualization support
Test CatBoost
R package installation
Install the released version
conda install
Build from source
Install from a local copy on Linux and macOS
Install from a local copy on Windows
Command-line version binary
Download
Build the binary from a local copy on Linux and macOS
Build the binary from a local copy on Windows
Build the binary with make on Linux (CPU only)
Build the binary with MPI support from a local copy (GPU only)
Python package
Quick start
Training parameters
CatBoost
fit
predict
Attributes
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
set_scale_and_bias
set_feature_names
set_params
shrink
staged_predict
CatBoostClassifier
fit
predict
predict_proba
Attributes
calc_feature_statistics