sklearn.discriminant_analysis
.QuadraticDiscriminantAnalysis¶

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: priors : array, optional, shape = [n_classes]
Priors on classes
reg_param : float, optional
Regularizes the covariance estimate as
(1reg_param)*Sigma + reg_param*np.eye(n_features)
store_covariance : boolean
If True the covariance matrices are computed and stored in the self.covariance_ attribute.
New in version 0.17.
tol : float, optional, default 1.0e4
Threshold used for rank estimation.
New in version 0.17.
Attributes: covariance_ : list of arraylike, shape = [n_features, n_features]
Covariance matrices of each class.
means_ : arraylike, shape = [n_classes, n_features]
Class means.
priors_ : arraylike, shape = [n_classes]
Class priors (sum to 1).
rotations_ : list of arrays
For each class k an array of shape [n_features, n_k], with
n_k = min(n_features, number of elements in class k)
It is the rotation of the Gaussian distribution, i.e. its principal axis.scalings_ : list of arrays
For each class k an array of shape [n_k]. It contains the scaling of the Gaussian distributions along its principal axes, i.e. the variance in the rotated coordinate system.
See also
sklearn.discriminant_analysis.LinearDiscriminantAnalysis
 Linear Discriminant Analysis
Examples
>>> 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]
Methods
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)[source]¶

covariances_
¶ DEPRECATED: Attribute covariances_ was deprecated in version 0.19 and will be removed in 0.21. Use covariance_ instead

decision_function
(X)[source]¶ Apply decision function to an array of samples.
Parameters: X : arraylike, 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 twoclass case, the shape is [n_samples,], giving the log likelihood ratio of the positive class.

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 asstore_covariance
Changed in version 0.19:
tol
has been moved to main constructor.Parameters: X : arraylike, shape = [n_samples, n_features]
Training vector, where n_samples is the number of samples and n_features is the number of features.
y : array, shape = [n_samples]
Target values (integers)

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.

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 : arraylike, shape = [n_samples, n_features] Returns: C : array, shape = [n_samples]

predict_log_proba
(X)[source]¶ Return posterior probabilities of classification.
Parameters: X : arraylike, shape = [n_samples, n_features]
Array of samples/test vectors.
Returns: C : array, shape = [n_samples, n_classes]
Posterior logprobabilities of classification per class.

predict_proba
(X)[source]¶ Return posterior probabilities of classification.
Parameters: X : arraylike, 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)[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: X : arraylike, shape = (n_samples, n_features)
Test samples.
y : arraylike, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
sample_weight : arraylike, shape = [n_samples], optional
Sample weights.
Returns: score : float
Mean accuracy of self.predict(X) wrt. y.

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 :