sklearn调用朴素贝叶斯.predict（）报错dimension mismatch

Danidy

于 2019-03-29 16:33:43 发布

阅读量7k

点赞数 19

分类专栏： python 文章标签： sklearn

本文链接：https://blog.youkuaiyun.com/qq_28334183/article/details/88896111

版权

1、报错

运行sklearn包中自带的朴素贝叶斯进行预测的时候提示了ValueError: dimension mismatch

def NaiveBayes():
    """
    朴素贝叶斯进行文本分类
    :return: None
    """
    news = fetch_20newsgroups(subset='all')
    print(news.data)
    print(news.target)

    # 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.fit_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))

    return None