from sklearn import datasets
from sklearn.naive_bayes import GaussianNB

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

03125bb97d8529385139010077863598e4e760f4
<mask token>

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

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

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

from sklearn import datasets
from sklearn.naive_bayes import GaussianNB

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