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from sklearn import datasets
iris = datasets.load_iris()
from sklearn.cross_validation 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=0)

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       0.93      1.00      0.96        13
          2       1.00      0.83      0.91         6

avg / total       0.97      0.97      0.97        30

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