class sklearn.naive_bayes.GaussianNB(priors=None, var_smoothing=1e09)
[source]
Gaussian Naive Bayes (GaussianNB)
Can perform online updates to model parameters via partial_fit
method. For details on algorithm used to update feature means and variance online, see Stanford CS tech report STANCS79773 by Chan, Golub, and LeVeque:
Read more in the User Guide.
Parameters: 


Attributes: 

>>> 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(priors=None, var_smoothing=1e09) >>> print(clf.predict([[0.8, 1]])) [1] >>> clf_pf = GaussianNB() >>> clf_pf.partial_fit(X, Y, np.unique(Y)) GaussianNB(priors=None, var_smoothing=1e09) >>> print(clf_pf.predict([[0.8, 1]])) [1]
fit (X, y[, sample_weight])  Fit Gaussian Naive Bayes according to X, y 
get_params ([deep])  Get parameters for this estimator. 
partial_fit (X, y[, classes, sample_weight])  Incremental fit on a batch of samples. 
predict (X)  Perform classification on an array of test vectors X. 
predict_log_proba (X)  Return logprobability 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__(priors=None, var_smoothing=1e09)
[source]
fit(X, y, sample_weight=None)
[source]
Fit Gaussian Naive Bayes according to X, y
Parameters: 


Returns: 

get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

partial_fit(X, y, classes=None, sample_weight=None)
[source]
Incremental fit on a batch of samples.
This method is expected to be called several times consecutively on different chunks of a dataset so as to implement outofcore or online learning.
This is especially useful when the whole dataset is too big to fit in memory at once.
This method has some performance and numerical stability overhead, hence it is better to call partial_fit on chunks of data that are as large as possible (as long as fitting in the memory budget) to hide the overhead.
Parameters: 


Returns: 

predict(X)
[source]
Perform classification on an array of test vectors X.
Parameters: 


Returns: 

predict_log_proba(X)
[source]
Return logprobability estimates for the test vector X.
Parameters: 


Returns: 

predict_proba(X)
[source]
Return probability estimates for the test vector X.
Parameters: 


Returns: 

score(X, y, sample_weight=None)
[source]
Returns the mean accuracy on the given test data and labels.
In multilabel classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters: 


Returns: 

set_params(**params)
[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
Returns: 


sklearn.naive_bayes.GaussianNB
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html