1 from sklearn import datasets
 2 iris = datasets.load_iris()
 3 from sklearn.naive_bayes import GaussianNB
 4 gnb = GaussianNB()
 5 y_pred = gnb.fit(iris.data, iris.target).predict(iris.data)
 6 print("Number of mislabeled points out of a total %d points : %d"
 7        % (iris.data.shape[0],(iris.target != y_pred).sum()))
 
 8 
 9 
10 from sklearn.naive_bayes import MultinomialNB
11 clf = MultinomialNB(alpha=0.5)
12 y_pred1=clf.fit(iris.data, iris.target).predict(iris.data)
13 print("Number of mislabeled points out of a total %d points : %d"
14        % (iris.data.shape[0],(iris.target != y_pred1).sum()))
15 
16 a1=clf.fit(iris.data, iris.target).predict_proba(iris.data)
 
17 a2=clf.fit(iris.data, iris.target).predict_log_proba(iris.data)

sklearn 樸素貝葉斯