In [14]:

    

from sklearn import datasets


iris = datasets.load_iris()
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=1)

from sklearn.naive_bayes import GaussianNB
clf = GaussianNB()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

from sklearn.metrics import classification_report
print (classification_report(y_test, y_pred))


    

    




             precision    recall  f1-score   support

          0       1.00      1.00      1.00        11
          1       1.00      0.92      0.96        13
          2       0.86      1.00      0.92         6

avg / total       0.97      0.97      0.97        30

In [13]:

    

import sklearn
print (sklearn.__version__)


    

    




0.18.2

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