机器学习——朴素贝叶斯

 在考虑一个结果的概率时候，要考虑众多的属性，贝叶斯算法利用所有可能的数据来进行修正预测，如果大量的特征产生的影响较小，放在一起，组合的影响较大，适合于朴素贝叶斯分类

导入类库

1 from sklearn.datasets import fetch_20newsgroups
2 from sklearn.feature_extraction.text import tfidfvectorizer
3 from sklearn.naive_bayes import multinomialnb

代码

 1 def article_category():
 2     categories = ['alt.atheism', 'soc.religion.christian', 'comp.graphics', 'sci.med']
 3     twenty_train = fetch_20newsgroups(subset='train', categories=categories)
 4     # print(twenty_train)
 5     print(twenty_train.data)
 6     print(twenty_train.target)
 7     # 将x训练集词频向量化
 8     tfidf_transformer = tfidfvectorizer()
 9     x_train_tfidf = tfidf_transformer.fit_transform(twenty_train.data)
10 
11     # 贝叶斯训练
12     clf = multinomialnb(alpha=1.0).fit(x_train_tfidf, twenty_train.target)
13     docs_new = ['chemical reaction', 'intel cpu is good']
14 
15     # 将要预测的数据词频向量化
16     x_new_tfidf = tfidf_transformer.transform(docs_new)
17     # 预测
18     predicted = clf.predict(x_new_tfidf)
19     print(predicted)
20     for doc, category in zip(docs_new, predicted):
21         print('%r => %s' % (doc, twenty_train.target_names[category]))