lale.lib.sklearn.dummy_classifier module

class lale.lib.sklearn.dummy_classifier.DummyClassifier(*, strategy='prior', random_state=None, constant=None)

Bases: PlannedIndividualOp

Dummy classifier classifier that makes predictions using simple rules.

This documentation is auto-generated from JSON schemas.

Parameters

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

    Strategy to use to generate predictions.

    • ’stratified’

      Generates predictions by respecting the training set’s class distribution.

    • or ‘most_frequent’

      Always predicts the most frequent label in the training set.

    • or ‘prior’

      Always predicts the class that maximizes the class prior (like ‘most_frequent’) and predict_proba returns the class prior.

    • or ‘uniform’

      Generates predictions uniformly at random.

    • 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.

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

    Seed of pseudo-random number generator for shuffling data when solver == ‘sag’, ‘saga’ or ‘liblinear’.

    • None

      RandomState used by np.random

    • or numpy.random.RandomState

      Use the provided random state, only affecting other users of that same random state instance.

    • or integer

      Explicit seed.

  • 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.

    • string or number or boolean

    • or None

    See also constraint-1.

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

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

  • y (union type) –

    Target class labels.

    • array of items : string

    • or array of items : float

    • or array of items : boolean

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 –

  • array of items : string

  • or array of items : float

  • or array of items : boolean

Return type

union type

predict_proba(X)

Probability estimates for all classes.

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

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

Parameters

X (array of items : array of items : Any) – Features; the outer array is over samples.

Returns

result

Return type

array of items : array of items : float