关于测试与测试的困惑机器学习中的验证集标签 [英] Confusion about test & validation set labels in machine learning

问题描述

我对数据集的训练和验证有疑问.

I have a question with regards to the training and validation of a dataset.

我了解用于训练数据(即y_train)的标签的概念.我不明白的是，为什么我们的测试/验证样本也要有标签. 我认为，通过给测试样本加上标签，我们可以确定它们是什么，然后再对算法进行正确处理?

I understand the concept of labels for training data i.e. y_train. What I don't get is that why should our testing/validation samples have labels as well. I assume that by giving labels to the test samples, we define what they are before putting them through the algorithm right?

如果我有一个狗和猫的图片数据集，并且分别将它们标记为1和2，就让我这样说.然后，如果我想扔一张照片(狗)来测试我的模型(该模型不在我的训练数据集中)，为什么要标记它?如果我将其标记为1，那么我就事先告诉它是狗，如果我将其标记为2，则它已经是猫.

Let me put it this way, if I have a dataset of pictures of dogs and cats, and I label them 1 and 2, respectively. Then if I want to throw a picture (dog) to test my model, which was not in my training dataset, why should I label it? If I label it 1, then I'm telling beforehand that it's a dog and if I label it 2, then it is a cat already.

我可以有没有标签的测试/验证数据集吗?

Can I have a testing/validation dataset without label?

推荐答案

验证数据集用于微调模型中的参数，而测试集用于检查准确性.没有标签，怎么可以声称模型的正确性.这一概念在监督学习中有效，因此需要带有测试和验证数据集的标签.

Validation dataset is used to finetune the parameters in your model while the test set is used to check the accuracy. Without the label how can claim the correctness of your model. This concept is valid in supervised learning so one needs to have labels with testing and validation dataset.

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