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

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

from sklearn.datasets import load_iris
iris = load_iris()
from sklearn.naive_bayes import GaussianNB   #高斯
gnb = GaussianNB()  #构造
pre = gnb.fit(iris.data,iris.target)  #拟合
y_pre = gnb.predict(iris.data)   #预测
print(iris.data.shape[0],(iris.target != y_pre).sum())
scores = cross_val_score(gnb,iris.data,iris.target,cv=10) #评估
print("Accuracy:%.3f"%scores.mean())


from sklearn.naive_bayes import BernoulliNB  #伯努利
bnb = BernoulliNB()
pre = bnb.fit(iris.data,iris.target)
y_pre = bnb.predict(iris.data)
print(iris.data.shape[0],(iris.target != y_pre).sum())
scores = cross_val_score(bnb,iris.data,iris.target,cv=10) 
print("Accuracy:%.3f"%scores.mean())


from sklearn.naive_bayes import MultinomialNB  #多项式
mnb = MultinomialNB()
pre = mnb.fit(iris.data,iris.target)
y_pre = mnb.predict(iris.data)
print(iris.data.shape[0],(iris.target != y_pre).sum())
scores = cross_val_score(mnb,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_data.append(line[1])
sms.close()
sms_data

sms_label

• 对邮件内容进行预处理：去掉长度小于3的词，去掉没有语义的词等

 

import nltk
nltk.download()
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatiser

 

训练集和测试集数据划分

• from sklearn.model_selection import train_test_split

from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(sms_data,sms_label,test_size = 0.3,random_state=0,stratify=sms_label)

x_train
x_test