snowflake.ml.modeling.ensemble.AdaBoostRegressor¶
- class snowflake.ml.modeling.ensemble.AdaBoostRegressor(*, estimator=None, n_estimators=50, learning_rate=1.0, loss='linear', random_state=None, base_estimator='deprecated', input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶
Bases:
BaseTransformerAn AdaBoost regressor For more details on this class, see sklearn.ensemble.AdaBoostRegressor
- estimator: object, default=None
The base estimator from which the boosted ensemble is built. If
None, then the base estimator isDecisionTreeRegressorinitialized with max_depth=3.- n_estimators: int, default=50
The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early. Values must be in the range [1, inf).
- learning_rate: float, default=1.0
Weight applied to each regressor at each boosting iteration. A higher learning rate increases the contribution of each regressor. There is a trade-off between the learning_rate and n_estimators parameters. Values must be in the range (0.0, inf).
- loss: {‘linear’, ‘square’, ‘exponential’}, default=’linear’
The loss function to use when updating the weights after each boosting iteration.
- random_state: int, RandomState instance or None, default=None
Controls the random seed given at each estimator at each boosting iteration. Thus, it is only used when estimator exposes a random_state. In addition, it controls the bootstrap of the weights used to train the estimator at each boosting iteration. Pass an int for reproducible output across multiple function calls. See Glossary.
- base_estimator: object, default=None
The base estimator from which the boosted ensemble is built. If
None, then the base estimator isDecisionTreeRegressorinitialized with max_depth=3.- input_cols: Optional[Union[str, List[str]]]
A string or list of strings representing column names that contain features. If this parameter is not specified, all columns in the input DataFrame except the columns specified by label_cols and sample-weight_col parameters are considered input columns.
- label_cols: Optional[Union[str, List[str]]]
A string or list of strings representing column names that contain labels. This is a required param for estimators, as there is no way to infer these columns. If this parameter is not specified, then object is fitted without labels(Like a transformer).
- output_cols: Optional[Union[str, List[str]]]
A string or list of strings representing column names that will store the output of predict and transform operations. The length of output_cols mus match the expected number of output columns from the specific estimator or transformer class used. If this parameter is not specified, output column names are derived by adding an OUTPUT_ prefix to the label column names. These inferred output column names work for estimator’s predict() method, but output_cols must be set explicitly for transformers.
- sample_weight_col: Optional[str]
A string representing the column name containing the examples’ weights. This argument is only required when working with weighted datasets.
- drop_input_cols: Optional[bool], default=False
If set, the response of predict(), transform() methods will not contain input columns.
Methods
fit(dataset)Build a boosted classifier/regressor from the training set (X, y) For more details on this function, see sklearn.ensemble.AdaBoostRegressor.fit
predict(dataset)Predict regression value for X For more details on this function, see sklearn.ensemble.AdaBoostRegressor.predict
score(dataset)Return the coefficient of determination of the prediction For more details on this function, see sklearn.ensemble.AdaBoostRegressor.score
set_input_cols(input_cols)Input columns setter.
to_sklearn()Get sklearn.ensemble.AdaBoostRegressor object.
Attributes
model_signaturesReturns model signature of current class.