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

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。

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())

 

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())

 

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

数据准备：

• 用csv读取邮件数据，分解出邮件类别及邮件内容。

import csv
file_path=r'C:\Users\Administrator\Desktop\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.close()
print(len(sms_label))
sms_label