11.sklearn中的朴素贝叶斯模型及其应用

#1.使用朴素贝叶斯模型对iris数据集进行花分类
#尝试使用3种不同类型的朴素贝叶斯：
#高斯分布型,多项式型,伯努利型

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

150  6

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

150  100

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

150  7

#2.使用sklearn.model_selection.cross_val_score()，对模型进行验证。

#检测模型的好坏BernoulliNB
from sklearn.naive_bayes import BernoulliNB
from sklearn.model_selection import cross_val_score
gnb = BernoulliNB()
scores=cross_val_score(gnb,iris.data,iris.target,cv=)
print("Accuray:%.3f"%scores.mean())

Accuray:0.333

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

Accuray:0.953

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=)
print("Accuray:%.3f"%scores.mean())

Accuray:0.953