sklearn.naive_bayes.GaussianNB

class sklearn.naive_bayes.GaussianNB

Gaussian Naive Bayes (GaussianNB)

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 vector relative to X

Examples

>>> 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])
>>> from sklearn.naive_bayes import GaussianNB
>>> clf = GaussianNB()
>>> clf.fit(X, Y)
GaussianNB()
>>> print(clf.predict([[-0.8, -1]]))
[1]

Attributes

class_prior_	array, shape = [n_classes]	probability of each class.
theta_	array, shape = [n_classes, n_features]	mean of each feature per class
sigma_	array, shape = [n_classes, n_features]	variance of each feature per class

Methods

fit(X, y)	Fit Gaussian Naive Bayes according to X, y
get_params([deep])	Get parameters for this estimator.
predict(X)	Perform classification on an array of test vectors X.
predict_log_proba(X)	Return log-probability estimates for the test vector X.
predict_proba(X)	Return probability estimates for the test vector X.
score(X, y)	Returns the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of this estimator.

__init__()

x.__init__(...) initializes x; see help(type(x)) for signature

fit(X, y)

Fit Gaussian Naive Bayes according to X, y

Parameters :

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

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

y : array-like, shape = [n_samples]

Target values.

Returns :

self : object

Returns self.

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.

Parameters :

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

Returns :

C : array, shape = [n_samples]

Predicted target values for X

predict_log_proba(X)

Return log-probability estimates for the test vector X.

Parameters :

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

Returns :

C : array-like, shape = [n_samples, n_classes]

Returns the log-probability of the sample for each class in the model, where classes are ordered arithmetically.

predict_proba(X)

Return probability estimates for the test vector X.

Parameters :

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

Returns :

C : array-like, shape = [n_samples, n_classes]

Returns the probability of the sample for each class in the model, where classes are ordered arithmetically.

score(X, y)

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

Parameters :

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

Test samples.

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

True labels for X.

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 former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns :self :