CatBoostClassifier
Сlass CatBoostClassifier(iterations=None,
learning_rate=None,
depth=None,
l2_leaf_reg=None,
model_size_reg=None,
rsm=None,
loss_function='Logloss',
border_count=None,
feature_border_type=None,
old_permutation_block_size=None,
od_pval=None,
od_wait=None,
od_type=None,
nan_mode=None,
counter_calc_method=None,
leaf_estimation_iterations=None,
leaf_estimation_method=None,
thread_count=None,
random_seed=None,
use_best_model=None,
verbose=None,
logging_level=None,
metric_period=None,
ctr_leaf_count_limit=None,
store_all_simple_ctr=None,
max_ctr_complexity=None,
has_time=None,
allow_const_label=None,
classes_count=None,
class_weights=None,
one_hot_max_size=None,
random_strength=None,
name=None,
ignored_features=None,
train_dir=None,
custom_loss=None,
custom_metric=None,
eval_metric=None,
bagging_temperature=None,
save_snapshot=None,
snapshot_file=None,
snapshot_interval=None,
fold_len_multiplier=None,
used_ram_limit=None,
gpu_ram_part=None,
allow_writing_files=None,
final_ctr_computation_mode=None,
approx_on_full_history=None,
boosting_type=None,
simple_ctr=None,
combinations_ctr=None,
per_feature_ctr=None,
task_type=None,
device_config=None,
devices=None,
bootstrap_type=None,
subsample=None,
max_depth=None,
n_estimators=None,
num_boost_round=None,
num_trees=None,
colsample_bylevel=None,
random_state=None,
reg_lambda=None,
objective=None,
eta=None,
max_bin=None,
scale_pos_weight=None,
gpu_cat_features_storage=None,
data_partition=None
metadata=None,
early_stopping_rounds=None,
cat_features=None)
 Purpose

Training and applying models for the classification problems. When using the applying methods only the probability that the object belongs to the class is returned. Provides compatibility with the scikitlearn tools.
 Parameters

Parameter Description Default value metadata The keyvalue string pairs to store in the model's metadata storage after the training. None cat_features A onedimensional array of categorical columns indices.
Categorical features of the catboost.Pool object must be equal to those of the model if a catboost.Pool object is used for training.
Note. Do not use this parameter if the input training dataset (specified in the X parameter) type is catboost.Pool.None (all features are considered numerical) Parameter Description Default value metadata The keyvalue string pairs to store in the model's metadata storage after the training. None cat_features A onedimensional array of categorical columns indices.
Categorical features of the catboost.Pool object must be equal to those of the model if a catboost.Pool object is used for training.
Note. Do not use this parameter if the input training dataset (specified in the X parameter) type is catboost.Pool.None (all features are considered numerical) See Training parameters for the full list of parameters.
 Attributes

Attribute Description tree_count_ Return the number of trees in the model.
feature_importances_ Output the calculated feature importances.
random_seed_ The random seed used for training.
learning_rate_ The learning rate used for training.
feature_names_ The names of features in the dataset.
evals_result_ Return the values of metrics calculated during the training.
best_score_ Return the best result for each metric calculated on each validation dataset.
best_iteration_ Return the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set.
Attribute Description tree_count_ Return the number of trees in the model.
feature_importances_ Output the calculated feature importances.
random_seed_ The random seed used for training.
learning_rate_ The learning rate used for training.
feature_names_ The names of features in the dataset.
evals_result_ Return the values of metrics calculated during the training.
best_score_ Return the best result for each metric calculated on each validation dataset.
best_iteration_ Return the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set.
 Methods

Method Description fit Train a model.
predict Apply the model to the given dataset.
predict_proba Apply the model to the given dataset to predict the probability that the object belongs to the given classes.
staged_predict Apply the model to the given dataset and calculate the results for each ith tree of the model taking into consideration only the trees in the range
[1;i]
.staged_predict_proba Apply the model to the given dataset to predict the probability that the object belongs to the class and calculate the results for each ith tree of the model taking into consideration only the trees in the range
[1;i]
.eval_metrics Calculate the specified metrics for the specified dataset.
get_feature_importance Calculate and return the feature importances.
get_object_importance Calculate the effect of objects from the train dataset on the optimized metric values for the objects from the input dataset: Positive values reflect that the optimized metric increases.
 Negative values reflect that the optimized metric decreases.
load_model Load the model from a file.
save_model Save the model to a file.
shrink Shrink the model. Only trees with indices from the range
[ntree_start, ntree_end)
are kept.get_param Return the value of the specified training parameter.
get_params Return the training parameters.
set_params Set the training parameters.
score Calculate the Accuracy metric for the objects in the given dataset.
copy Copy the CatBoost object.
get_evals_result Return the values of metrics calculated during the training.
get_test_eval Return the formula values that were calculated for the objects from the validation dataset provided for training.
is_fitted Check whether the model is trained.
get_metadata Return a proxy object with metadata from the model's internal keyvalue string storage. get_best_score Return the best result for each metric calculated on each validation dataset.
get_best_iteration Return the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set.
Method Description fit Train a model.
predict Apply the model to the given dataset.
predict_proba Apply the model to the given dataset to predict the probability that the object belongs to the given classes.
staged_predict Apply the model to the given dataset and calculate the results for each ith tree of the model taking into consideration only the trees in the range
[1;i]
.staged_predict_proba Apply the model to the given dataset to predict the probability that the object belongs to the class and calculate the results for each ith tree of the model taking into consideration only the trees in the range
[1;i]
.eval_metrics Calculate the specified metrics for the specified dataset.
get_feature_importance Calculate and return the feature importances.
get_object_importance Calculate the effect of objects from the train dataset on the optimized metric values for the objects from the input dataset: Positive values reflect that the optimized metric increases.
 Negative values reflect that the optimized metric decreases.
load_model Load the model from a file.
save_model Save the model to a file.
shrink Shrink the model. Only trees with indices from the range
[ntree_start, ntree_end)
are kept.get_param Return the value of the specified training parameter.
get_params Return the training parameters.
set_params Set the training parameters.
score Calculate the Accuracy metric for the objects in the given dataset.
copy Copy the CatBoost object.
get_evals_result Return the values of metrics calculated during the training.
get_test_eval Return the formula values that were calculated for the objects from the validation dataset provided for training.
is_fitted Check whether the model is trained.
get_metadata Return a proxy object with metadata from the model's internal keyvalue string storage. get_best_score Return the best result for each metric calculated on each validation dataset.
get_best_iteration Return the identifier of the iteration with the best result of the evaluation metric or loss function on the last validation set.