python人工智能——机器学习——分类算法朴素贝叶斯算法对新闻进行分类案例

朴素贝叶斯案例流程

1、加载20类新闻数据，并进行分割

2、生成文章特征词

3、朴素贝叶斯estimator流程进行预估

代码

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

def naviebayes():
    """
    朴素贝叶斯进行文本分类
    :return: None
    """
    news = fetch_20newsgroups(subset='all')

    # 进行数据分割
    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("准确率为：", 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()

P：

关于sklearn.datasets.fetch_20newsgroups的下载速度极慢的解决
https://segmentfault.com/a/1190000016498146?utm_source=tag-newest