11.26

from sklearn.model_selection import cross_val_score
from sklearn.datasets import load_iris
iris = load_iris()
 
from sklearn.naive_bayes import GaussianNB#高斯
gnb = GaussianNB()
pred = gnb.fit(iris.data,iris.target)#构造模型
y_pred = pred.predict(iris.data)#预测
print(iris.data.shape[0],(iris.target!=y_pred).sum())
score=cross_val_score(gnb,iris.data,iris.target,cv=10)
print("准确率:%.3f"%score.mean())
 
from sklearn.naive_bayes import BernoulliNB#伯努利
bnl = BernoulliNB()
pred = bnl.fit(iris.data,iris.target)#构造模型
y_pred = pred.predict(iris.data)#预测
print(iris.data.shape[0],(iris.target!=y_pred).sum())
score=cross_val_score(bnl,iris.data,iris.target,cv=10)
print("准确率:%.3f"%score.mean())
 
from sklearn.naive_bayes import MultinomialNB#多项式
mlt = MultinomialNB()
pred = mlt.fit(iris.data,iris.target)#构造模型
y_pred = pred.predict(iris.data)#预测
print(iris.data.shape[0],(iris.target!=y_pred).sum())
score=cross_val_score(mlt,iris.data,iris.target,cv=10)
print("准确率:%.3f"%score.mean())

import csv
file_path=r‘SMSSpamCollectionjsn.txt‘
sms=open(file_path,‘r‘,encoding=‘utf-8‘)
sms_data=[]
sms_label=[]
csv_reader=csv.reader(sms,delimiter=‘\t‘)
for line in csv_reader:
    sms_label.append(line[0])
    sms_data.append(line[1])
sms.close()