Warning
DEPRECATED
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class sklearn.qda.QDA(priors=None, reg_param=0.0, store_covariances=False, tol=0.0001)[source] -
Alias for
sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.Deprecated since version 0.17: This class will be removed in 0.19. Use
sklearn.discriminant_analysis.QuadraticDiscriminantAnalysisinstead.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)[source]
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decision_function(X)[source] -
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.
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fit(X, y, store_covariances=None, tol=None)[source] -
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)
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get_params(deep=True)[source] -
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.
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predict(X)[source] -
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]
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predict_log_proba(X)[source] -
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.
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predict_proba(X)[source] -
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.
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score(X, y, sample_weight=None)[source] -
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.
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set_params(**params)[source] -
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 :
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