In [1]:

    

from sklearn import datasets as dts
import numpy as np


    

In [2]:

    

from sklearn.model_selection import cross_val_score
def error(X, y, estimator):
    return cross_val_score(estimator, X, y, n_jobs=-1).mean()


    

In [3]:

    

data = dts.load_breast_cancer()
target = data['target']
data = data['data']


    

In [4]:

    

from sklearn import naive_bayes


    

In [5]:

    

error(data, target, naive_bayes.BernoulliNB())


    

    
Out[5]:





0.62742040285899936

In [6]:

    

error(data, target, naive_bayes.MultinomialNB())


    

    
Out[6]:





0.89457904019307521

In [7]:

    

error(data, target, naive_bayes.GaussianNB())


    

    
Out[7]:





0.9367492806089297

In [12]:

    

data = dts.load_digits()
target = data['target']
data = data['data']


    

In [13]:

    

error(data, target, naive_bayes.BernoulliNB())


    

    
Out[13]:





0.82582365077805819

In [14]:

    

error(data, target, naive_bayes.MultinomialNB())


    

    
Out[14]:





0.87087714897350532

In [15]:

    

error(data, target, naive_bayes.GaussianNB())


    

    
Out[15]:





0.81860038035501381

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