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# sklearn.naive_bayes.GaussianNB¶

class sklearn.naive_bayes.GaussianNB

Gaussian Naive Bayes (GaussianNB)

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

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]]))

```

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[, sample_weight]) 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. 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. 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] 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] C : array-like, shape = [n_samples, n_classes] Returns the log-probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.
predict_proba(X)

Return probability estimates for the test vector X.

Parameters: X : array-like, shape = [n_samples, n_features] C : array-like, shape = [n_samples, n_classes] Returns the probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.
score(X, y, sample_weight=None)

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. sample_weight : array-like, shape = [n_samples], optional Sample weights. 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 :