使用训练/测试集进行交叉验证
是否可以通过交叉验证和使用训练/测试集进行评估？我理解交叉验证与保留评估，但我对是否将它们结合在一起感到困惑。
Is it possible to do the evaluation with cross validation and using training/testing sets? I understand cross validation vs holdout evaluation, but I am confused about if we combine them together.
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交叉验证和保留评估都广泛用于估计模型的准确性（或其他一些性能度量）。通常，如果您拥有大量可用数据，您可能会使用保留评估，但如果您受到更多限制，您可能会使用交叉验证。
但它们也可以用于其他目的，特别是模型选择和优化，人们通常可能想做这些事情以及估计模型的准确性。
例如，您可能希望对模型进行特征选择（在模型的多个版本中进行选择，每个版本都是使用不同的变量子集构建的），然后评估最终选择的模型。对于最终评估，您可以保留一个测试集用于保留验证；但为了选择最佳的变量子集，您可以比较在每个子集上构建的模型的准确性（通过训练集的交叉验证来估计）。
模型的其他方面也可以使用这种混合方法来优化，例如来自神经网络的复杂性参数或来自岭回归的岭参数。
希望有帮助！
Both cross-validation and holdout evaluation are widely used for estimating the accuracy (or some other measure of performance) of a model. Typically, if you have the luxury of a large amount of data available, you might use holdout evaluation, but if you are a bit more restricted, you might use cross-validation.
But they can also be used for other purposes - in particular, model selection and optimization - and one might commonly want to do these things as well as estimating the model's accuracy.
For example, you might wish to carry out feature selection on your model (choose among several versions of the model, each if which has been built with a different subset of variables), and then evaluate the final chosen model. For the final evaluation, you might reserve a test set for holdout validation; but in order to choose the best subset of variables, you might compare the accuracies of the models built on each subset, as estimated by a cross-validation on the training set.
Other aspects of models could also be optimized using this mixed approach such as, for example, a complexity parameter from a neural network or the ridge parameter from ridge regression.
Hope that helps!