sklearn中的朴素贝叶斯模型及其应用

from sklearn import datasets
iris = datasets.load_iris()
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
pred = gnb.fit(iris.data,iris.target)
y_pred = pred.predict(iris.data)
print (iris.data.shape[0],(iris.target != y_pred).sum())

iris.target

y_pred

from sklearn import datasets
iris = datasets.load_iris()
from sklearn.naive_bayes import BernoulliNB
gnb = BernoulliNB()
pred = gnb.fit(iris.data,iris.target)
y_pred = pred.predict(iris.data)
print (iris.data.shape[0],(iris.target != y_pred).sum())

iris.target




y_pred



from sklearn import datasets
iris = datasets.load_iris()
from sklearn.naive_bayes import MultinomialNB
gnb = MultinomialNB()
pred = gnb.fit(iris.data,iris.target)
y_pred = pred.predict(iris.data)
print (iris.data.shape[0],(iris.target != y_pred).sum())



from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import cross_val_score
gnb=GaussianNB()
scores=cross_val_score(gnb,iris.data,iris.target,cv=10)
print("Accuracy:%.3f"%scores.mean())




scores