Resampling Techniques for Cross-Validation in Machine Learning

In machine learning, cross-validation is often performed to select the best hyper-parameter for a model. Once the hyper-parameters are selected, the model is retrained on both train and validation sets before being evaluated with the test set. A general workflow for cross-validation looks something like this:

Source: Scikit-learn