snowy1205: 这个代码大概运行了多久啊，我这边一共30轮，一轮就要十几分钟（CPU计算），这种情况能优化不？

夏贤一: 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 naivebayes(): """ 朴素贝叶斯进行文本分类 :return: None """ news = fetch_20newsgroups(subset="all") # 对数据集进行划分 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() x_train = tf.fit_transform(x_train) # 打印特征名称（如果有需要的话） # print(tf.get_feature_names()) x_test = tf.transform(x_test) # 进行朴素贝叶斯算法 mlt = MultinomialNB(alpha=1.0) mlt.fit(x_train, y_train) # 打印训练数据的稀疏表示（如果有需要的话） # print(x_train.toarray()) y_predict = mlt.predict(x_test) print("预测的文章类别为：", y_predict) # 获得准确率 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__": naivebayes()

ju37737: 谢谢！

ssj3070863284: 您好，请问一下，该如何设置柱体的宽度？

kklxkk: 博主这个代码是不是要运行很久啊