交叉验证-使用测试集或验证集进行预测? [英] Cross Validation--Use testing set or validation set to predict?

问题描述

我有一个关于交叉验证的问题.

I have a question about cross validation.

在机器学习中，我们知道有训练、验证和测试集.测试集是最终运行以查看最终模型/分类器的性能.

In Machine learning, we know there're training, validation, test set. And test set is final run to see how the final model/classifier performed.

但是在交叉验证过程中:我们将数据分为训练集和测试集(大多数教程都使用此术语)，所以我很困惑.我们是否需要将整个数据分为三个部分:培训，验证，测试?由于在交叉验证中，我们一直在谈论与两个集合的关系:训练和另一个.

But in the process of cross validation: we are splitting data into training set and testing set(most tutorial used this term), so I'm confused. Do we need to split the whole data into 3 parts: training, validation, test? Since in cross validation we just keep talking about relationship with 2 set: training and the other.

有人可以帮助澄清吗?

谢谢

推荐答案

通常这是一个或非"选择.根据设计，交叉验证的过程是验证模型的另一种方法.您不需要单独的验证集-各种火车测试分区的交互取代了对验证集的需求.

This is generally an either-or choice. The process of cross-validation is, by design, another way to validate the model. You don't need a separate validation set -- the interactions of the various train-test partitions replace the need for a validation set.

考虑名称​​ cross -validation ...:-)

Think about the name, cross-validation ... :-)

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