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module mlforecast.distributed.models.ray.lgb

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class RayLGBMForecast

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property best_iteration_

:obj:int: The best iteration of fitted model if early_stopping() callback has been specified.

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property best_score_

:obj:dict: The best score of fitted model.

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property booster_

Booster: The underlying Booster of this model.

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property evals_result_

:obj:dict: The evaluation results if validation sets have been specified.

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property feature_importances_

:obj:array of shape = [n_features]: The feature importances (the higher, the more important). .. note: 

     ``importance_type`` attribute is passed to the function      to configure the type of importance values to be extracted. 

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#### <kbd>property</kbd> feature_name_

:obj:`list` of shape = [n_features]: The names of features. 

.. note:
If input does not contain feature names, they will be added during fitting in the format Column_0, Column_1, …, Column_N.

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property feature_names_in_

:obj:array of shape = [n_features]: scikit-learn compatible version of .feature_name_. .. versionadded:: 4.5.0

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property model_

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property n_estimators_

:obj:int: True number of boosting iterations performed. This might be less than parameter n_estimators if early stopping was enabled or if boosting stopped early due to limits on complexity like min_gain_to_split. .. versionadded:: 4.0.0

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property n_features_

:obj:int: The number of features of fitted model.

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property n_features_in_

:obj:int: The number of features of fitted model.

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property n_iter_

:obj:int: True number of boosting iterations performed. This might be less than parameter n_estimators if early stopping was enabled or if boosting stopped early due to limits on complexity like min_gain_to_split. .. versionadded:: 4.0.0

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property objective_

:obj:str or :obj:callable: The concrete objective used while fitting this model.