sklearn.naive_bayes.GaussianNB  
class sklearn.naive_bayes.GaussianNB(*, priors=None, var_smoothing=1e-09) [source]
 
Gaussian Naive Bayes (GaussianNB) Can perform online updates to model parameters via partial_fit. For details on algorithm used to update feature means and variance online, see Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque: http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf Read more in the User Guide.  Parameters 
 
priorsarray-like of shape (n_classes,) 

Prior probabilities of the classes. If specified the priors are not adjusted according to the data.  
var_smoothingfloat, default=1e-9 

Portion of the largest variance of all features that is added to variances for calculation stability.  New in version 0.20.     Attributes 
 
class_count_ndarray of shape (n_classes,) 

number of training samples observed in each class.  
class_prior_ndarray of shape (n_classes,) 

probability of each class.  
classes_ndarray of shape (n_classes,) 

class labels known to the classifier  
epsilon_float 

absolute additive value to variances  
sigma_ndarray of shape (n_classes, n_features) 

variance of each feature per class  
theta_ndarray of shape (n_classes, n_features) 

mean 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]]))
[1]
>>> clf_pf = GaussianNB()
>>> clf_pf.partial_fit(X, Y, np.unique(Y))
GaussianNB()
>>> print(clf_pf.predict([[-0.8, -1]]))
[1]
 Methods  
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 log-probability estimates for the test vector X.  
predict_proba(X) Return probability estimates for the test vector X.  
score(X, y[, sample_weight]) Return the mean accuracy on the given test data and labels.  
set_params(**params) Set the parameters of this estimator.    
fit(X, y, sample_weight=None) [source]
 
Fit Gaussian Naive Bayes according to X, y  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Training vectors, where n_samples is the number of samples and n_features is the number of features.  
yarray-like of shape (n_samples,) 

Target values.  
sample_weightarray-like of shape (n_samples,), default=None 

Weights applied to individual samples (1. for unweighted).  New in version 0.17: Gaussian Naive Bayes supports fitting with sample_weight.     Returns 
 
selfobject 
   
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

If True, will return the parameters for this estimator and contained subobjects that are estimators.    Returns 
 
paramsdict 

Parameter names mapped to their values.     
  
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 out-of-core 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 
 
Xarray-like of shape (n_samples, n_features) 

Training vectors, where n_samples is the number of samples and n_features is the number of features.  
yarray-like of shape (n_samples,) 

Target values.  
classesarray-like of shape (n_classes,), default=None 

List of all the classes that can possibly appear in the y vector. Must be provided at the first call to partial_fit, can be omitted in subsequent calls.  
sample_weightarray-like of shape (n_samples,), default=None 

Weights applied to individual samples (1. for unweighted).  New in version 0.17.     Returns 
 
selfobject 
   
  
predict(X) [source]
 
Perform classification on an array of test vectors X.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 
  Returns 
 
Cndarray of shape (n_samples,) 

Predicted target values for X     
  
predict_log_proba(X) [source]
 
Return log-probability estimates for the test vector X.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 
  Returns 
 
Carray-like of 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) [source]
 
Return probability estimates for the test vector X.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 
  Returns 
 
Carray-like of 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) [source]
 
Return the mean accuracy on the given test data and labels. In multi-label 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 
 
Xarray-like of shape (n_samples, n_features) 

Test samples.  
yarray-like of shape (n_samples,) or (n_samples, n_outputs) 

True labels for X.  
sample_weightarray-like of shape (n_samples,), default=None 

Sample weights.    Returns 
 
scorefloat 

Mean accuracy of self.predict(X) wrt. y.     
  
set_params(**params) [source]
 
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.  Parameters 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.     
 
 Examples using sklearn.naive_bayes.GaussianNB
 
  Comparison of Calibration of Classifiers  

  Probability Calibration curves  

  Probability calibration of classifiers  

  Classifier comparison  

  Plot class probabilities calculated by the VotingClassifier  

  Plotting Learning Curves  

  Importance of Feature Scaling