概率：概率定义为一件事情发生的可能性
如：扔硬币某一面朝上有50%概率
“朴素”贝叶斯：所有特征之间条件独立
朴素贝叶斯-文档分类
from sklearn.datasets import fetch_20newsgroupsfrom sklearn.model_selection import train_test_splitfrom sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.naive_bayes import MultinomialNBdef 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))return Noneif __name__=="__main__":naviebayes()
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