cas-pml/ML/unterlagen/08_NB_Iris.ipynb

In [1]:

from sklearn import datasets
iris = datasets.load_iris()
print(iris.data.shape)

(150, 4)

In [2]:

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=0)
print(X_train.shape)
print(y_train.shape)

(120, 4)
(120,)

In [3]:

from sklearn.naive_bayes import GaussianNB
nb = GaussianNB()
nb.fit(X_train, y_train)
nb.score(X_test,y_test)

Out [3]:

0.96666666666666667

In [4]:

from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
dt.score(X_test,y_test)

Out [4]:

1.0

In [10]:

from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import cross_val_score
nb2 = GaussianNB()
scores = cross_val_score(nb2, iris.data, iris.target, cv=10)
print("Accuracy 0.95 confidence interval: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))

Accuracy 0.95 confidence interval: 0.95 (+/- 0.09)

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