8.25.1. sklearn.qda.QDA

class sklearn.qda.QDA(priors=None)

Quadratic Discriminant Analysis (QDA)

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.

Parameters :

priors : array, optional, shape = [n_classes]

Priors on classes

See also

sklearn.lda.LDA

Linear discriminant analysis

Examples

>>> from sklearn.qda import QDA
>>> 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 = QDA()
>>> clf.fit(X, y)
QDA(priors=None)
>>> print clf.predict([[-0.8, -1]])
[1]

Attributes

means_	array-like, shape = [n_classes, n_features]	Class means
priors_	array-like, shape = [n_classes]	Class priors (sum to 1)
covariances_	list of array-like, shape = [n_features, n_features]	Covariance matrices of each class

Methods

decision_function(X)	Apply decision function to an array of samples.
fit(X, y[, store_covariances, tol])	Fit the QDA model according to the given training data and parameters.
get_params([deep])	Get parameters for the 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)	Returns the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of the estimator.

__init__(priors=None)

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]

Decision function values related to each class, per sample.

fit(X, y, store_covariances=False, tol=0.0001)

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

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)

store_covariances : boolean

If True the covariance matrices are computed and stored in the self.covariances_ attribute.

get_params(deep=True)

Get parameters for the estimator

Parameters :

deep: boolean, optional :

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

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)

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

Parameters :

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

Training set.

y : array-like, shape = [n_samples]

Labels for X.

Returns :

z : float

set_params(**params)

Set the parameters of the estimator.

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

Returns :self :