小编Cur*_*Cat的帖子

我有以下代码,它使用NB分类器来解决多类分类问题.该函数通过存储精度并稍后打印平均值来预先进行交叉验证.我想要的是一个分类报告,指定类别的精确度和召回,而不是最终的平均准确度分数.

   import random
   from sklearn import cross_validation
   from sklearn.naive_bayes import MultinomialNB

   def multinomial_nb_with_cv(x_train, y_train):
        random.shuffle(X)
        kf = cross_validation.KFold(len(X), n_folds=10)
        acc = []
        for train_index, test_index in kf:
            y_true = y_train[test_index]
            clf = MultinomialNB().fit(x_train[train_index],         
            y_train[train_index])
            y_pred = clf.predict(x_train[test_index])
            acc.append(accuracy_score(y_true, y_pred))

如果我不进行交叉验证,我所要做的就是:

    from sklearn.metrics import classification_report
    from sklearn.naive_bayes import MultinomialNB

    def multinomial_nb(x_train, y_train, x_test, y_test):
        clf = MultinomialNB().fit(x_train, y_train)
        y_pred = clf.predict(x_test)
        y_true = y_test
        print classification_report(y_true, y_pred)

它给了我一个这样的报告:

        precision    recall  f1-score   support

      0       0.50      0.24      0.33       221
      1       0.00      0.00      0.00 …