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“ OneVsRestClassifier”（Scikit学习）和“ Binary Relevance”（Scikit多学习）之间有什么区别？

[英]What is the difference between “OneVsRestClassifier” (Scikit-learn) and “Binary Relevance” (Scikit-multilearn)?

原文 2019-07-25 19:22:28 2 1 machine-learning/ scikit-learn/ multilabel-classification/ scikit-multilearn

In scikit-learn, there is a strategy called sklearn.multiclass.OneVsRestClassifier , which can be used for both multiclass and multilabel problems. 在scikit-learn中，有一种称为sklearn.multiclass.OneVsRestClassifier的策略，该策略可用于多类和多sklearn.multiclass.OneVsRestClassifier问题。 According to its documentation : 根据其文档：

"In the multilabel learning literature, OvR is also known as the binary relevance method". “在多标签学习文献中，OvR也被称为二进制相关方法”。

My question is, 我的问题是

Is there is any difference between this scikit-learn strategy and skmultilearn.problem_transform.BinaryRelevance ? 这种scikit学习策略与skmultilearn.problem_transform.BinaryRelevance之间有什么区别吗？

Thank you in advance. 先感谢您。

1 个解决方案

No, there is no difference. 不，没有区别。 They work in the exact same way. 它们以完全相同的方式工作。 They both predict probabilities for an instance belonging to a class, yes or no. 它们都预测属于某个类的实例的概率，是或否。

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