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CatBoost
 
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
Applying models
C/C++
Java
CoreML
ONNX
Rust
.NET
ClickHouse
PMML
Models exported as code
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_test_eval
grid_search
is_fitted
load_model
plot_tree
randomized_search
save_model
save_borders
set_feature_names
set_params
shrink
staged_predict
CatBoostClassifier
fit
predict
predict_proba
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_test_eval
grid_search
is_fitted
load_model
plot_tree
randomized_search
save_borders
save_model
score
set_feature_names
set_params
shrink
staged_predict
staged_predict_proba
CatBoostRegressor
fit
predict
Attributes
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_test_eval
grid_search
is_fitted
load_model
plot_tree
randomized_search
save_borders
save_model
score
set_feature_names
set_params
shrink
staged_predict
cv
datasets
adult
amazon
epsilon
monotonic1
monotonic2
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_weight
num_col
num_row
set_baseline
set_feature_names
set_group_id
set_group_weight
set_pairs
set_pairs_weight
set_subgroup_id
set_weight
slice
Pool initialization
sum_models
train
utils
create_cd
eval_metric
get_gpu_device_count
get_fnr_curve
get_fpr_curve
get_roc_curve
select_threshold
Usage examples
R package
Quick start
catboost.load_pool
catboost.save_pool
catboost.train
catboost.load_model
catboost.save_model
catboost.predict
catboost.shrink
catboost.staged_predict
catboost.get_feature_importance
catboost.get_object_importance
catboost.get_model_params
Training parameters
Attributes
Usage examples
Command-line version
Train a model
Cross-validation
Apply a model
Calculate metrics
Calculate feature importance
Calculate object importance
Metadata manipulation
Sum models
Usage examples
Applying models
C/C++
Evaluation library
Standalone evaluator
Java
CatBoostModel
loadModel
getPredictionDimension
getTreeCount
getUsedCategoricFeatureCount
getUsedNumericFeatureCount
predict
close
CatBoostPredictions
copyRowMajorPredictions
copyObjectPredictions
get
getObjectCount
getPredictionDimension
CoreML
ONNX
Rust
.NET
ClickHouse
PMML
Models exported as code
C++
Python
Objectives and metrics
Regression
Classification
Multiclassification
Ranking
Model analysis
Feature importance
ShapValues
Feature analysis graphs
Feature interaction
Object importance
Data format description
Input data
Columns description
Dataset description
Pairs description
Custom quantization borders and missing value modes
Group weights
Baseline
Output data
Model values
Feature analysis
Feature importance
Feature interaction strength
ShapValues
Objects strength
Metrics and time information
Profiler information
Metric
Time information
stdout
Custom quantization borders and missing value modes
ROC curve points
Parameter tuning
Speeding up the training
Data visualization
Jupyter Notebook
TensorBoard
FAQ
Educational materials
Tutorials
Reference papers
Videos
Development and contributions
Algorithm details
How training is performed
Preliminary calculation of splits
Transforming categorical features to numerical features
Choosing the tree structure
Bootstrap options
Unbiased boosting
Quantization
Overfitting detector
Recovering training after an interruption
Missing values processing
Score functions
Contacts
Applying models

Applying models

The following non-standard methods for applying the models are supported:
  • C/C++:
    • Evaluation library
    • Standalone evaluator
  • Java
  • CoreML
  • ONNX
  • Rust
  • .NET
  • ClickHouse
  • PMML
  • Models exported as code:
    • C++
    • Python