利用贝叶斯分类器对fetch_20newsgroups数据集进行分类

from sklearn.datasets import fetch_20newsgroups
from sklearn.cross_validation import train_test_split
from  sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB 
from sklearn.metrics import classification_report


news = fetch_20newsgroups(subset='all')#本次使用的数据需要到互联网上下载
#查看数据
print(len(news.data))
print(news.data[0])
#对数据训练集和测试件进行划分
X_train,X_test,y_train,y_test = train_test_split(news.data,news.target,test_size=0.25,random_state=33)
vec = CountVectorizer()
X_train = vec.fit_transform(X_train)
X_test = vec.transform(X_test)
#利用贝叶斯分类器对数据进行分类
mnb = MultinomialNB()
mnb.fit(X_train,y_train)
y_predict = mnb.predict(X_test)
print('The accuracy of Naive Bays Classifier is',mnb.score(X_test,y_test))
print(classification_report(y_test,y_predict,target_names=news.target_names))