朴素贝叶斯分类器预测文本练习

        sklearn里集成了一个数据集，里面有20类新闻的文本数据，而朴素贝叶斯算法在文本分类的应用场景中十分实用。今天就用naive_bayes来练习一下文本分类。上码：

#-*- coding:utf-8 -*-

from sklearn.datasets import fetch_20newsgroups

news=fetch_20newsgroups(subset='all')

print(len(news.data))

print(news.data[0])

from sklearn.cross_validation import train_test_split

X_train,X_test,y_train,y_test=train_test_split(news.data,news.target,test_size=0.25,random_state=33)

#-------CountVectorizer这个模块是用于对文本抽取特征向量

from sklearn.feature_extraction.text import CountVectorizer

vec=CountVectorizer()

X_train=vec.fit_transform(X_train)

X_test=vec.transform(X_test)

from sklearn.naive_bayes import MultinomialNB

NB=MultinomialNB()

NB.fit(X_train,y_train)

y_predict=NB.predict(X_train)

#-------性能分析

print('朴素贝叶斯分类准确率：',NB.score(X_test,y_test))

from sklearn.metrics import classification_report

xnfx=classification_report(y_test,y_predict,target_names=news.target_names)

print(xnfx)