lale.lib.sklearn.ridge_classifier module

class lale.lib.sklearn.ridge_classifier.RidgeClassifier(*, alpha=1.0, fit_intercept=True, copy_X=True, max_iter=None, tol=0.0001, solver='auto', class_weight=None, random_state=None)

Bases: PlannedIndividualOp

Ridge classifier from scikit-learn.

This documentation is auto-generated from JSON schemas.

Parameters

  • alpha (union type, default 1.0) –

    Regularization strength; larger values specify stronger regularization.

    • float, >0.0, >=1e-05 for optimizer, <=10.0 for optimizer, loguniform distribution

    • or array, not for optimizer of items : float, >0.0

      Penalties specific to the targets.

  • fit_intercept (boolean, default True) – Whether to calculate the intercept for this model.

  • copy_X (boolean, optional, default True) – If True, X will be copied; else, it may be overwritten.

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

    Maximum number of iterations for conjugate gradient solver.

    • integer, >=10 for optimizer, <=1000 for optimizer

    • or None

  • tol (float, >=1e-08 for optimizer, <=0.01 for optimizer, optional, default 0.0001) – Precision of the solution.

  • solver (‘auto’, ‘svd’, ‘cholesky’, ‘lsqr’, ‘sparse_cg’, ‘sag’, or ‘saga’, default ‘auto’) – Solver to use in the computational routines.

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

    Weights associated with classes in the form {class_label: weight}.

    • dict

    • or ‘balanced’ or None

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

    The seed of the pseudo random number generator to use when shuffling

    • integer

    • or numpy.random.RandomState

    • or 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) – Training data

  • y (union type) –

    Target values

    • array of items : array of items : float

    • or array of items : float

    • or array of items : string

    • or array of items : boolean

  • sample_weight (union type, optional) –

    Individual weights for each sample

    • float

    • or 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 (union type, optional) –

Samples.

  • array of items : float

  • or array of items : array of items : float

Returns

result – Predicted class label per sample.

  • array of items : float

  • or array of items : string

  • or array of items : boolean

Return type

union type