说明：  在机器学习或者深度学习中，我们常常碰到一个问题是数据集的切分。比如在一个比赛中，举办方给我们的只是一个带标注的训练集和不带标注的测试集。其中训练集是用于训练，而测试集用于已训练模型上跑出一个结果，然后提交，然后举办方验证结果给出一个分数。但是我们在训练过程中，可能会出现过拟合等问题，会面临着算法和模型的选择，此时，验证集就显得很重要。通常，如果数据量充足，我们会从训练集中划分出一定比例的数据来作为验证集。
(In machine learning or deep learning, we often encounter the problem of data set segmentation. For example, in a competition, the organizer gives us only a labeled training set and an unlabeled test set. The training set is used for training, and the test set is used for running a result on the training model, then submitting it, and then the organizer verifies the result and gives a score. However, in the training process, we may have some problems such as fitting, and will face the choice of algorithms and models. At this time, verification set is very important. Usually, if the data is sufficient, we will divide a certain proportion of the data from the training set as the verification set.)