scikits.learn.naive_bayes.GNB

class scikits.learn.naive_bayes.GNB

Gaussian Naive Bayes (GNB)

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 scikits.learn.naive_bayes import GNB
>>> clf = GNB()
>>> clf.fit(X, Y)
GNB()
>>> print clf.predict([[-0.8, -1]])
[1]

Attributes

proba_y	array, shape = nb of classes	probability of each class.
theta	array of shape nb_class*nb_features	mean of each feature for the different class
sigma	array of shape nb_class*nb_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.

__init__()

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