snowflake.ml.modeling.ensemble.StackingRegressor¶

class snowflake.ml.modeling.ensemble.StackingRegressor(*, estimators, final_estimator=None, cv=None, n_jobs=None, passthrough=False, verbose=0, 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: BaseTransformer

Stack of estimators with a final regressor For more details on this class, see sklearn.ensemble.StackingRegressor

estimators: list of (str, estimator)

Base estimators which will be stacked together. Each element of the list is defined as a tuple of string (i.e. name) and an estimator instance. An estimator can be set to ‘drop’ using set_params.

final_estimator: estimator, default=None

A regressor which will be used to combine the base estimators. The default regressor is a RidgeCV.

cv: int, cross-validation generator, iterable, or “prefit”, default=None

Determines the cross-validation splitting strategy used in cross_val_predict to train final_estimator. Possible inputs for cv are:

• None, to use the default 5-fold cross validation,

• integer, to specify the number of folds in a (Stratified) KFold,

• An object to be used as a cross-validation generator,

• An iterable yielding train, test splits.

• “prefit” to assume the estimators are prefit, and skip cross validation

For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used. These splitters are instantiated with shuffle=False so the splits will be the same across calls.

Refer User Guide for the various cross-validation strategies that can be used here.

If “prefit” is passed, it is assumed that all estimators have been fitted already. The final_estimator_ is trained on the estimators predictions on the full training set and are not cross validated predictions. Please note that if the models have been trained on the same data to train the stacking model, there is a very high risk of overfitting.

n_jobs: int, default=None

The number of jobs to run in parallel for fit of all estimators. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

passthrough: bool, default=False

When False, only the predictions of estimators will be used as training data for final_estimator. When True, the final_estimator is trained on the predictions as well as the original training data.

verbose: int, default=0

Verbosity level.

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)	Fit the estimators For more details on this function, see sklearn.ensemble.StackingRegressor.fit
predict(dataset)	Predict target for X For more details on this function, see sklearn.ensemble.StackingRegressor.predict
score(dataset)	Return the coefficient of determination of the prediction For more details on this function, see sklearn.ensemble.StackingRegressor.score
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.ensemble.StackingRegressor object.
transform(dataset)	Return the predictions for X for each estimator For more details on this function, see sklearn.ensemble.StackingRegressor.transform

Attributes

model_signatures

Returns model signature of current class.