catboost/docs/en/concepts/python-reference_catboostregressor.md
class CatBoostRegressor(iterations=None,
learning_rate=None,
depth=None,
l2_leaf_reg=None,
model_size_reg=None,
rsm=None,
loss_function='RMSE',
border_count=None,
feature_border_type=None,
per_float_feature_quantization=None,
input_borders=None,
output_borders=None,
fold_permutation_block=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,
best_model_min_trees=None,
verbose=None,
silent=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,
one_hot_max_size=None,
random_strength=None,
name=None,
ignored_features=None,
train_dir=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,
pinned_memory_size=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,
ctr_target_border_count=None,
task_type=None,
device_config=None,
devices=None,
bootstrap_type=None,
subsample=None,
sampling_unit=None,
dev_score_calc_obj_block_size=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,
gpu_cat_features_storage=None,
data_partition=None,
metadata=None,
early_stopping_rounds=None,
cat_features=None,
grow_policy=None,
min_data_in_leaf=None,
min_child_samples=None,
max_leaves=None,
num_leaves=None,
score_function=None,
leaf_estimation_backtracking=None,
ctr_history_unit=None,
monotone_constraints=None,
feature_weights=None,
penalties_coefficient=None,
first_feature_use_penalties=None,
model_shrink_rate=None,
model_shrink_mode=None,
langevin=None,
diffusion_temperature=None,
posterior_sampling=None,
boost_from_average=None,
fixed_binary_splits=None)
Implementation of the scikit-learn estimator API for CatBoost regression.
Supports model training, inference and auxiliary calculations like feature importance.
{% include python__parameters__metadata %}
{% include python__parameters__cat_features %}
{% include sections-with-methods-desc-see-training-params %}
{% include precedence-python--regressor--precedence-note %}
{% include tree_count_-tree_count__desc %}
{% include feature_importances_-feature_importances__desc %}
{% include random_seed_-random_seed__desc %}
{% include learning_rate_-learning_rate__desc %}
{% include feature_names_-feature_names__desc %}
{% include sections-with-methods-desc-python__get-evals-result__desc %}
{% include sections-with-methods-desc-python__method__get_best_score__desc %}
{% include sections-with-methods-desc-python__method__get_best_iteration__desc %}
{% include sections-with-methods-desc-fit--purpose-desc %}
{% include sections-with-methods-desc-predict--purpose %}
{% include python-reference_catboost_calc_leaf_indexes-calc_leaf_indexes__desc %}
{% include get_feature_statistics-get_feature_statistics__desc__div %}
{% include sections-with-methods-desc-copy--purpose %}
{% include model-compare-compare__purpose %}
{% include sections-with-methods-desc-python__eval-metrics__purpose %}
{% include get_all_params-python__get_all_params__desc__p %}
{% include sections-with-methods-desc-python__method__get_best_iteration__desc %}
{% include sections-with-methods-desc-python__method__get_best_score__desc %}
{% include get_borders-get_borders__desc__div %}
{% include sections-with-methods-desc-python__get-evals-result__desc %}
{% include sections-with-methods-desc-feature_importances--purpose %}
Return a proxy object with metadata from the model's internal key-value string storage.
{% include sections-with-methods-desc-python__get_object_importance__desc %}
{% include sections-with-methods-desc-get_param--purpose %}
{% include sections-with-methods-desc-get_params--purpose %}
{% include get_scale_and_bias-get_scale_and_bias__desc %}
{% include sections-with-methods-desc-get_test_val--desc %}
{% include grid_search-python__grid_search--desc %}
{% include sections-with-methods-desc-is_fitted--purpose %}
{% include sections-with-methods-desc-load_model--purpose %}
{% include plot_predictions-plot_predictions__desc__short %}
{% include plot_tree-python__plot-tree_desc__div %}
{% include randomized_search-python__randomized_search--desc %}
{% include save_borders-save_model__div_desc %}
{% include sections-with-methods-desc-save_model--purpose %}
{% include catboost-regressor-score-scor__purpose %}
Select the best features from the dataset using the Recursive Feature Elimination algorithm.
{% include non_pool__set_feature_names-non_pool__set_feature_names__div %}
{% include sections-with-methods-desc-set_params--purpose %}
{% include set_scale_and_bias-set_scale_and_bias__desc %}
{% include sections-with-methods-desc-shrink__purpose %}
{% include sections-with-methods-desc-staged_predict--purpose %}