朴素贝叶斯实战新闻组数据集
关于朴素贝叶斯的相关理论知识可查看：朴素贝叶斯法
关于新闻组数据集
20newsgroups数据集是用于文本分类、文本挖据和信息检索研究的国际标准数据集之一。一些新闻组的主题特别相似(e.g. comp.sys.ibm.pc.hardware/comp.sys.mac.hardware)，还有一些却完全不相关 (e.g misc.forsale /soc.religion.christian)。
20个新闻组数据集包含大约18000个新闻组，其中20个主题分成两个子集:一个用于训练(或开发)，另一个用于测试(或用于性能评估)。训练集和测试集之间的分割是基于特定日期之前和之后发布的消息。
代码实战
首先，还是导入数据集
from sklearn.datasets import fetch_20newsgroups
news = fetch_20newsgroups(subset='all')
print(len(news.data))
print(news.data[0])我们这里打印出来一个新闻例子，如下
18846
From: Mamatha Devineni Ratnam [email protected]
Subject: Pens fans reactions
Organization: Post Office, Carnegie Mellon, Pittsburgh, PA
Lines: 12
NNTP-Posting-Host: po4.andrew.cmu.eduI am sure some bashers of Pens fans are pretty confused about the lack
of any kind of posts about the recent Pens massacre of the Devils. Actually,
I am bit puzzled too and a bit relieved. However, I am going to put an end
to non-PIttsburghers’ relief with a bit of praise for the Pens. Man, they
are killing those Devils worse than I thought. Jagr just showed you why
he is much better than his regular season stats. He is also a lot
fo fun to watch in the playoffs. Bowman should let JAgr have a lot of
fun in the next couple of games since the Pens are going to beat the pulp out of Jersey anyway. I was very disappointed not to see the Islanders lose the final
regular season game. PENS RULE!!!
接下来，划分数据集，还是75%训练集，25%测试集
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)我们需要对文本特征进行提取，我们这里使用CountVectorizer来提取特征。CountVectorizer能够将文本词块化，通过计算词汇的数量来将文本转化成向量（更多文本特征提取内容可查看https://www.cnblogs.com/Haichao-Zhang/p/5220974.html）。然后我们导入模型来学习数据。
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
mnb = MultinomialNB()
mnb.fit(X_train,Y_train)
y_predict = mnb.predict(X_test)最后，我们还是一样，检验一下模型的准确度
from sklearn.metrics import classification_report
print('The Accuracy of Navie Bayes Classifier is',mnb.score(X_test,Y_test))
print(classification_report(Y_test,y_predict,target_names = news.target_names))
代码参考：《Python机器学习及实践：从零开始通往Kaggle竞赛之路》