lale.lib.sklearn.quadratic_discriminant_analysis module

class lale.lib.sklearn.quadratic_discriminant_analysis.QuadraticDiscriminantAnalysis(*, priors=None, reg_param=0.0, store_covariance=False, tol=0.0001)

Bases: PlannedIndividualOp

Quadratic discriminant analysis classifier with a quadratic decision boundary from scikit-learn.

This documentation is auto-generated from JSON schemas.

Parameters

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

  Priors on classes

  • array of items : float

  • or None

• reg_param (float, >=0.0 for optimizer, <=1.0 for optimizer, uniform distribution, optional, default 0.0) – Regularizes the covariance estimate as

• store_covariance (boolean, not for optimizer, default False) – If True the covariance matrices are computed and stored in the

• tol (float, >=1e-08 for optimizer, <=0.01 for optimizer, optional, default 0.0001) – Threshold used for rank estimation.

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 vector, where n_samples is the number of samples and

• y (union type) –

  Target values (integers)

  • array of items : float

  • or array of items : string

  • 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 : float) –

Returns

result – Perform classification on an array of test vectors X.

• 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) – Array of samples/test vectors.

Returns

result – Posterior probabilities of classification per class.

Return type

array of items : array of items : float