lale.lib.sklearn.dummy_regressor module

class lale.lib.sklearn.dummy_regressor.DummyRegressor(*, strategy='mean', constant=None, quantile=None)

Bases: PlannedIndividualOp

Dummy regressor regressor that makes predictions using simple rules.

This documentation is auto-generated from JSON schemas.

Parameters

• strategy (union type, not for optimizer, default 'mean') –

  Strategy to use to generate predictions.

  • ’mean’

    Always predicts the mean of the training set.

  • or ‘median’

    Always predicts the median of the training set.

  • or ‘quantile’, not for optimizer

    Always predicts a specified quantile of the training set, provided with the quantile parameter.

  • or ‘constant’, not for optimizer

    Always predicts a constant label that is provided by the user. This is useful for metrics that evaluate a non-majority class

  See also constraint-1, constraint-2.

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

  The explicit constant as predicted by the “constant” strategy. This parameter is useful only for the “constant” strategy.

  • float

  • or None

  See also constraint-1.

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

  The quantile to predict using the “quantile” strategy. A quantile of 0.5 corresponds to the median, while 0.0 to the minimum and 1.0 to the maximum.

  • None

  • or float, >=0.0, <=1.0

  See also constraint-2.

Notes

constraint-1 : union type

The constant strategy requires a non-None value for the constant hyperparameter.

• strategy : negated type of ‘constant’

• or constant : negated type of None

constraint-2 : union type

The quantile strategy requires a non-None value for the quantile hyperparameter.

• strategy : negated type of ‘quantile’

• or quantile : negated type of None

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) – Features; the outer array is over samples.

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

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 : Any) – Features; the outer array is over samples.

Returns

result – Predicted values per sample.

Return type

array of items : float