lale.lib.sklearn.voting_regressor module

class lale.lib.sklearn.voting_regressor.VotingRegressor(*, estimators, weights=None, n_jobs=None, verbose=False)

Bases: PlannedIndividualOp

Voting classifier from scikit-learn for voting ensemble.

This documentation is auto-generated from JSON schemas.

Parameters

  • estimators (array, not for optimizer) –

    List of (string, estimator) tuples. Invoking the fit method on the VotingClassifier will fit clones.

    • items : tuple

      • item 0 : string

      • item 1 : union type

        • operator

        • or ‘drop’

  • weights (union type, default None) –

    Sequence of weights (float or int) to weight the occurrences of

    • array of items : float

    • or None

  • n_jobs (union type, not for optimizer, default None) –

    The number of jobs to run in parallel for fit.

    • integer

    • or None

  • verbose (boolean, optional, not for optimizer, default False) – If True, the time elapsed while fitting will be printed as it is completed.

fit(X, y=None, **fit_params)

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

  • X (array of items : array of items : float) – Input samples.

  • y (array of items : float) – Target values.

  • sample_weight (union type, optional) –

    Sample weights. If None, then samples are equally weighted.

    • array of items : float

    • or None

predict(X, **predict_params)

Make predictions.

Note: The predict method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (array, optional of items : array of items : float) – The input samples.

Returns

result – Predicted class labels.

Return type

array of items : float

transform(X, y=None)

Transform the data.

Note: The transform method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (array, optional of items : array of items : float) – Input samples

Returns

result – Values predicted by each regressor

  • items : array

    • items : union type

      • float

      • or array of items : float

Return type

array