from sklearn.datasets import fetch_20newsgroups from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import classification_report def naviebayes(): # 准备数据 news = fetch_20newsgroups(subset="all") print(news.data) print(news.target) # 数据分割 x_train,x_test,y_train,y_test = train_test_split(news.data,news.target,test_size=0.25) # 对数据集进行特征抽取 tf = TfidfVectorizer() # 以训练集当中的词的列表进行每篇文章的重要性统计 x_train = tf.fit_transform(x_train) print(tf.get_feature_names()) x_test = tf.transform(x_test) # 进行朴素贝叶斯算法的预测 mlt = MultinomialNB(alpha=1.0) print(x_train.toarray()) mlt.fit(x_train,y_train) y_predict= mlt.predict(x_test) print("预测的文章类别为：",y_predict) # 得出准确率 print("准确率：",mlt.score(x_test,y_test)) print("每个类别的精确率和召回率:",classification_report(y_test,y_predict,target_names=news.target_names)) return None if __name__=="__main__": naviebayes()
