BoostCB

class lightautoml.ml_algo.boost_cb.BoostCB(default_params=None, freeze_defaults=True, timer=None, optimization_search_space={})[source]

Bases: TabularMLAlgo, ImportanceEstimator

Gradient boosting on decision trees from catboost library.

All available parameters listed in CatBoost documentation:

freeze_defaults:

  • True : params may be rewritten depending on dataset.

  • False: params may be changed only manually or with tuning.

timer: Timer instance or None.

init_params_on_input(train_valid_iterator)[source]

Get model parameters depending on input dataset parameters.

Parameters:

train_valid_iterator (TrainValidIterator) – Classic cv-iterator.

Return type:

dict

Returns:

Parameters of model.

fit_predict_single_fold(train, valid)[source]

Implements training and prediction on single fold.

Parameters:
Return type:

Tuple[CatBoost, ndarray]

Returns:

Tuple (model, predicted_values).

predict_single_fold(model, dataset)[source]

Predict of target values for dataset.

Parameters:
Return type:

ndarray

Returns:

Predicted target values.

get_features_score()[source]

Computes feature importance.

Computes as mean values of feature importance, provided by CatBoost (PredictionValuesChange), per all models.

Return type:

Series

Returns:

Series with feature importances.

fit(train_valid)[source]

Just to be compatible with ImportanceEstimator.

Parameters:

train_valid (TrainValidIterator) – Classic cv-iterator.