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如何告诉scikit-了解给出F-1 /精确/召回得分的标签（二进制分类）？

[英]How to tell scikit-learn for which label the F-1/precision/recall score is given (in binary classification)?

原文 2015-12-15 01:12:18 4 1 python/ machine-learning/ scikit-learn/ classification/ binary-data

As explained in this article , it matters for calculating the F-1 score (that is, for calculating recall and precision) whether those calculations are based on the positive or negative class. 如本文所述，计算F-1分数（即计算召回率和精确度）对于这些计算是基于正类还是负类很重要。 For example, if I have a skewed dataset with 1% labels of category A and 99% labels of category B and I am just assigning A the positive category and classify all test items as positive, my F-1 score will be very good. 例如，如果我有一个偏斜的数据集，其中1％的A类标签和99％的B类标签，我只是将A分类为正类别并将所有测试项目分类为正数，我的F-1分数将非常好。 How do I tell scikit-learn which category is the positive category in a binary classification? 如何告诉scikit-在二进制分类中了解哪个类别是正类别？ (If helpful, I can provide code.) （如果有帮助，我可以提供代码。）

1 个解决方案

For binary classification, sklearn.metrics.f1_score will by default make the assumption that 1 is the positive class, and 0 is the negative class. 对于二进制分类， sklearn.metrics.f1_score默认情况下假设1是正类，0是负类。 If you use those conventions ( 0 for category B, and 1 for category A), it should give you the desired behavior. 如果您使用的这些公约（ 0为B类，和1 A类），它应该给你所期望的行为。 It is possible to override this behavior by passing the pos_label keyword argument to the f1_score function. 可以通过将pos_label关键字参数传递给f1_score函数来覆盖此行为。

See: http://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html 请参阅： http ： //scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html

Scikit-learn 召回率和准确率 - Scikit-learn recall and precision

scikit-learn分类报告-精度和F分数定义不正确，并设置为0.0 - scikit-learn classification report - Precision and F-score are ill-defined and being set to 0.0

F1小于Scikit学习中的精度和召回率 - F1 smaller than both precision and recall in Scikit-learn

如何使用 scikit learn 计算多类案例的准确率、召回率、准确率和 f1 分数？ - How to compute precision, recall, accuracy and f1-score for the multiclass case with scikit learn?

计算多 label 分类 keras 的召回精度和 F1 分数 - compute the recall precision and F1 score for a multi label classification keras

如何在 scikit-learn 的分类问题中为 F1 分数做 GridSearchCV？ - How to do GridSearchCV for F1-score in classification problem with scikit-learn?

如何使用scikit-learn对二进制数据集进行分类？ - How to do classification in binary data set using scikit-learn?

没有二元分类器的 scikit-learn 中的 auc 分数 - auc score in scikit-learn for no binary classifiers

ResNet50：验证准确率高，但召回率、准确率和 f-1 分数表现不佳 - ResNet50: high validation accuracy but poor performances in recall, precision and f-1 score

Scikit-learn 错误地计算 recall_score - Scikit-learn incorrectly calculating recall_score

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相关问题 Scikit-learn 召回率和准确率 - Scikit-learn recall and precision scikit-learn分类报告-精度和F分数定义不正确，并设置为0.0 - scikit-learn classification report - Precision and F-score are ill-defined and being set to 0.0 F1小于Scikit学习中的精度和召回率 - F1 smaller than both precision and recall in Scikit-learn 如何使用 scikit learn 计算多类案例的准确率、召回率、准确率和 f1 分数？ - How to compute precision, recall, accuracy and f1-score for the multiclass case with scikit learn? 计算多 label 分类 keras 的召回精度和 F1 分数 - compute the recall precision and F1 score for a multi label classification keras 如何在 scikit-learn 的分类问题中为 F1 分数做 GridSearchCV？ - How to do GridSearchCV for F1-score in classification problem with scikit-learn? 如何使用scikit-learn对二进制数据集进行分类？ - How to do classification in binary data set using scikit-learn? 没有二元分类器的 scikit-learn 中的 auc 分数 - auc score in scikit-learn for no binary classifiers ResNet50：验证准确率高，但召回率、准确率和 f-1 分数表现不佳 - ResNet50: high validation accuracy but poor performances in recall, precision and f-1 score Scikit-learn 错误地计算 recall_score - Scikit-learn incorrectly calculating recall_score

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