class sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0, store_covariance=False, tol=0.0001, store_covariances=None)
[source]
Quadratic Discriminant Analysis
A classifier with a quadratic decision boundary, generated by fitting class conditional densities to the data and using Bayes’ rule.
The model fits a Gaussian density to each class.
New in version 0.17: QuadraticDiscriminantAnalysis
Read more in the User Guide.
Parameters: 


Attributes: 

See also
sklearn.discriminant_analysis.LinearDiscriminantAnalysis
>>> from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis >>> import numpy as np >>> X = np.array([[1, 1], [2, 1], [3, 2], [1, 1], [2, 1], [3, 2]]) >>> y = np.array([1, 1, 1, 2, 2, 2]) >>> clf = QuadraticDiscriminantAnalysis() >>> clf.fit(X, y) ... QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0, store_covariance=False, store_covariances=None, tol=0.0001) >>> print(clf.predict([[0.8, 1]])) [1]
decision_function (X)  Apply decision function to an array of samples. 
fit (X, y)  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_covariance=False, tol=0.0001, store_covariances=None)
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covariances_
DEPRECATED: Attribute covariances_
was deprecated in version 0.19 and will be removed in 0.21. Use covariance_
instead
decision_function(X)
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Apply decision function to an array of samples.
Parameters: 


Returns: 

fit(X, y)
[source]
Fit the model according to the given training data and parameters.
Changed in version 0.19: store_covariances
has been moved to main constructor as store_covariance
Changed in version 0.19: tol
has been moved to main constructor.
Parameters: 


get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

predict(X)
[source]
Perform classification on an array of test vectors X.
The predicted class C for each sample in X is returned.
Parameters: 


Returns: 

predict_log_proba(X)
[source]
Return posterior probabilities of classification.
Parameters: 


Returns: 

predict_proba(X)
[source]
Return posterior probabilities of classification.
Parameters: 


Returns: 

score(X, y, sample_weight=None)
[source]
Returns the mean accuracy on the given test data and labels.
In multilabel 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: 


Returns: 

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: 


sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html