lale.lib.sklearn.ada_boost_classifier module

class lale.lib.sklearn.ada_boost_classifier.AdaBoostClassifier(*, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None, estimator=None)

Bases: PlannedIndividualOp

AdaBoost classifier from scikit-learn for boosting ensemble.

This documentation is auto-generated from JSON schemas.

Parameters

  • n_estimators (integer, >=50 for optimizer, <=500 for optimizer, uniform distribution, default 50) – The maximum number of estimators at which boosting is terminated.

  • learning_rate (float, >=0.01 for optimizer, <=1.0 for optimizer, loguniform distribution, default 1.0) – Learning rate shrinks the contribution of each classifier by

  • algorithm (union type, default 'SAMME.R') –

    The boosting algorithm to use

    • ’SAMME’

      Use the SAMME discrete boosting algorithm.

    • or ‘SAMME.R’

      deprecated

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

    If int, random_state is the seed used by the random number generator;

    • integer

    • or numpy.random.RandomState

    • or None

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

    The base estimator to fit on random subsets of the dataset.

    • operator

    • or None

      DecisionTreeClassifier

    See also constraint-1.

Notes

constraint-1 : union type

Only estimator or base_estimator should be specified. As base_estimator is deprecated, use estimator.

  • base_estimator : False or ‘deprecated’

  • or estimator : None

decision_function(X)

Confidence scores for all classes.

Note: The decision_function 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 : float) – Features; the outer array is over samples.

Returns

result – Confidence scores for samples for each class in the model.

  • array of items : array of items : float

    In the multi-way case, score per (sample, class) combination.

  • or array of items : float

    In the binary case, score for self._classes[1].

Return type

union type

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) – The training input samples. Sparse matrix can be CSC, CSR, COO,

  • y (union type) –

    The target values (class labels).

    • array of items : float

    • or array of items : string

    • or array of items : boolean

  • sample_weight (union type, optional, default None) –

    Sample weights. If None, the sample weights are initialized to

    • 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 training input samples. Sparse matrix can be CSC, CSR, COO,

Returns

result – The predicted classes.

  • array of items : float

  • or array of items : string

  • 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, optional of items : array of items : float) – The training input samples. Sparse matrix can be CSC, CSR, COO,

Returns

result – The class probabilities of the input samples. The order of

Return type

array of items : array of items : float