Machine learning models often face the problem of generalization when they're applied to unseen data to make predictions. To avoid this problem, the model isn't trained using the complete dataset. Instead, the dataset is split into training and testing subsets. The model is trained on the training data and evaluated on the testing set, which it doesn't see during the training process. This is the fundamental idea behind cross-validation.
The simplest kind of cross-validation is the holdout method, which we saw in the previous recipe, Introduction to sampling. In the holdout method, when we split our data into training and testing subsets, there's a possibility that the testing set isn't that similar to the training set because of the high dimensionality of the data. This can lead to instability in the outcome...
