9.3.2. 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 for the different class
sigma	array, shape [n_classes * n_features]	variance of each feature for the different class

Methods

fit(X, y)	self	Fit the model
predict(X)	array	Predict using the model.
predict_proba(X)	array	Predict the probability of each class using the model.
predict_log_proba(X)	array	Predict the log-probability of each class using the model.

__init__()

x.__init__(...) initializes x; see x.__class__.__doc__ 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.

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]

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 by arithmetical order.

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 by arithmetical order.

score(X, y)

Returns the mean error rate 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 :