sklearn.qda.QDA

Warning

DEPRECATED

class sklearn.qda.QDA(priors=None, reg_param=0.0, store_covariances=False, tol=0.0001)

Alias for sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.

Deprecated since version 0.17: This class will be removed in 0.19. Use sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis instead.

Methods

decision_function(X)	Apply decision function to an array of samples.
fit(X, y[, store_covariances, tol])	Fit the model according to the given training data and parameters.
get_params([deep])	Get parameters for this estimator.
predict(X)	Perform classification on an array of test vectors X.
predict_log_proba(X)	Return posterior probabilities of classification.
predict_proba(X)	Return posterior probabilities of classification.
score(X, y[, sample_weight])	Returns the mean accuracy on the given test data and labels.
set_params(\*\*params)	Set the parameters of this estimator.

__init__(priors=None, reg_param=0.0, store_covariances=False, tol=0.0001)

decision_function(X)

Apply decision function to an array of samples.

Parameters:

X : array-like, shape = [n_samples, n_features]

Array of samples (test vectors).

Returns:

C : array, shape = [n_samples, n_classes] or [n_samples,]

Decision function values related to each class, per sample. In the two-class case, the shape is [n_samples,], giving the log likelihood ratio of the positive class.

fit(X, y, store_covariances=None, tol=None)

Fit the model according to the given training data and parameters.

Changed in version 0.17: Deprecated store_covariance have been moved to main constructor.

Changed in version 0.17: Deprecated tol have been moved to main constructor.

Parameters:

X : array-like, shape = [n_samples, n_features]

Training vector, where n_samples in the number of samples and n_features is the number of features.

y : array, shape = [n_samples]

Target values (integers)

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

predict(X)

Perform classification on an array of test vectors X.

The predicted class C for each sample in X is returned.

Parameters:X : array-like, shape = [n_samples, n_features] Returns:C : array, shape = [n_samples]

predict_log_proba(X)

Return posterior probabilities of classification.

Parameters:

X : array-like, shape = [n_samples, n_features]

Array of samples/test vectors.

Returns:

C : array, shape = [n_samples, n_classes]

Posterior log-probabilities of classification per class.

predict_proba(X)

Return posterior probabilities of classification.

Parameters:

X : array-like, shape = [n_samples, n_features]

Array of samples/test vectors.

Returns:

C : array, shape = [n_samples, n_classes]

Posterior probabilities of classification per class.

score(X, y, sample_weight=None)

Returns the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

X : array-like, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples) or (n_samples, n_outputs)

True labels for X.

sample_weight : array-like, shape = [n_samples], optional

Sample weights.

Returns:

score : float

Mean accuracy of self.predict(X) wrt. y.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :