from sklearn import datasets

iris = datasets.load_iris()
from sklearn.naive_bayes import GaussianNB

clf = GaussianNB()
clf.fit(iris.data, iris.target)
y_pred = clf.predict(iris.data)
print("Number of mislabeled points out of a total %d points:%d" % (iris.data.shape[0], (iris.target != y_pred).sum()))

b65316ceac27c06eb4d774bea2a2d03e16feddb7
<mask token>

<mask token>
clf.fit(iris.data, iris.target)
<mask token>
print('Number of mislabeled points out of a total %d points:%d' % (iris.
    data.shape[0], (iris.target != y_pred).sum()))

<mask token>
iris = datasets.load_iris()
<mask token>
clf = GaussianNB()
clf.fit(iris.data, iris.target)
y_pred = clf.predict(iris.data)
print('Number of mislabeled points out of a total %d points:%d' % (iris.
    data.shape[0], (iris.target != y_pred).sum()))

from sklearn import datasets
iris = datasets.load_iris()
from sklearn.naive_bayes import GaussianNB
clf = GaussianNB()
clf.fit(iris.data, iris.target)
y_pred = clf.predict(iris.data)
print('Number of mislabeled points out of a total %d points:%d' % (iris.
    data.shape[0], (iris.target != y_pred).sum()))

from sklearn import datasets

iris = datasets.load_iris()
from sklearn.naive_bayes import GaussianNB

clf = GaussianNB()
clf.fit(iris.data, iris.target)
y_pred = clf.predict(iris.data)
print("Number of mislabeled points out of a total %d points:%d" % (iris.data.shape[0], (iris.target != y_pred).sum()))
