snowflake.ml.modeling.ensemble.VotingRegressor¶

class snowflake.ml.modeling.ensemble.VotingRegressor(*, estimators, weights=None, n_jobs=None, verbose=False, 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

Prediction voting regressor for unfitted estimators For more details on this class, see sklearn.ensemble.VotingRegressor

estimators: list of (str, estimator) tuples

Invoking the fit method on the VotingRegressor will fit clones of those original estimators that will be stored in the class attribute self.estimators_. An estimator can be set to 'drop' using set_params().

weights: array-like of shape (n_regressors,), default=None

Sequence of weights (float or int) to weight the occurrences of predicted values before averaging. Uses uniform weights if None.

n_jobs: int, default=None

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

verbose: bool, default=False

If True, the time elapsed while fitting will be printed as it is completed.

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

Attributes

model_signatures

Returns model signature of current class.