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
datasets
adult
amazon
epsilon
higgs
monotonic1
monotonic2
msrank
msrank_10k
rotten_tomatoes
titanic
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_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
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_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
staged_predict_proba
CatBoostRegressor
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
adult
amazon
epsilon
higgs
monotonic1
monotonic2
msrank
msrank_10k
rotten_tomatoes
titanic
FeaturesData
get_cat_feature_count
get_feature_count
get_feature_names
get_num_feature_count
get_object_count
MetricVisualizer
start
Pool
Attributes
get_baseline
get_cat_feature_indices
get_features
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_weight
slice
Pool initialization<