机器学习 - 贝叶斯新闻分类

最新推荐文章于 2024-10-18 13:14:49 发布

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最新推荐文章于 2024-10-18 13:14:49 发布

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分类专栏：机器学习 python 文章标签：机器学习朴素贝叶斯 python

本文链接：https://blog.youkuaiyun.com/u013044815/article/details/87449734

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本文介绍了使用朴素贝叶斯模型进行大规模互联网文本分类的方法，通过Sklearn库实现数据预处理、模型训练与评估，展示了模型在不同类别文本上的精度、召回率和F1分数。

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本博客内容来自《PYTHON机器学习及实践－从零开始通往KAGGLE竞赛之路》一书

from sklearn.datasets import fetch_20newsgroups
#下载数据
news = fetch_20newsgroups(subset='all')
print(news.data[0])

需要从互联网下载数据

From: Mamatha Devineni Ratnam <mr47+@andrew.cmu.edu>
Subject: Pens fans reactions
Organization: Post Office, Carnegie Mellon, Pittsburgh, PA
Lines: 12
NNTP-Posting-Host: po4.andrew.cmu.edu

I 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!!!

from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer#导入文本特征向量转化模块
from sklearn.naive_bayes import MultinomialNB #导入贝叶斯模型
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)

mnb = MultinomialNB()
mnb.fit(X_train,y_train)
y_predict = mnb.predict(X_test)

from sklearn.metrics import classification_report #导入模型评估模块
print('The accuracy of Naive Bayes classifier is ',mnb.score(X_test,y_test))
print(classification_report(y_test,y_predict,target_names=news.target_names))

The accuracy of Naive Bayes classifier is  0.8397707979626485
                          precision    recall  f1-score   support

             alt.atheism       0.86      0.86      0.86       201
           comp.graphics       0.59      0.86      0.70       250
 comp.os.ms-windows.misc       0.89      0.10      0.17       248
comp.sys.ibm.pc.hardware       0.60      0.88      0.72       240
   comp.sys.mac.hardware       0.93      0.78      0.85       242
          comp.windows.x       0.82      0.84      0.83       263
            misc.forsale       0.91      0.70      0.79       257
               rec.autos       0.89      0.89      0.89       238
         rec.motorcycles       0.98      0.92      0.95       276
      rec.sport.baseball       0.98      0.91      0.95       251
        rec.sport.hockey       0.93      0.99      0.96       233
               sci.crypt       0.86      0.98      0.91       238
         sci.electronics       0.85      0.88      0.86       249
                 sci.med       0.92      0.94      0.93       245
               sci.space       0.89      0.96      0.92       221
  soc.religion.christian       0.78      0.96      0.86       232
      talk.politics.guns       0.88      0.96      0.92       251
   talk.politics.mideast       0.90      0.98      0.94       231
      talk.politics.misc       0.79      0.89      0.84       188
      talk.religion.misc       0.93      0.44      0.60       158

               micro avg       0.84      0.84      0.84      4712
               macro avg       0.86      0.84      0.82      4712
            weighted avg       0.86      0.84      0.82      4712

朴素贝叶斯模型被广泛应用于海量互联网文本分类任务，由于其较强的特征条件独立假设，是的模型预测所需要估计的参数规模从幂指数级
向线性量级减少，极大节约了内存消耗和计算时间，但是也正是受这种强假设的限制，模型训练时无法将各个特征之间的联系考量在内，
使得该模型在其他数据特征关联性较强的分类任务上的性能表现不佳