sklearn中的朴素贝叶斯模型及其应用

  # 高斯分布型


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())

#多项式型
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())

#伯努利型
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())

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

#多项式型
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=10)
print("Accuracy:%.3f"%scores.mean())

#伯努利型
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=10)

print("Accuracy:%.3f"%scores.mean())

3. 垃圾邮件分类

import csv
file_path=r'F:SMSSpamCollectionjs.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()
sms_label
sms_data=str(sms_data)
sms_data=sms_data.lower()
sms_data=sms_data.split()
sms_newdata=[]
i=0
#去掉长度小于3的词
for i in sms_data:
    if len(i)>4:
        sms_newdata.append(i)
        continue
sms_newdata