In relation to resampling methods, which statements are correct?

The correct statement is that LOOCV (Leave-One-Out Cross-Validation) often overestimates test error rates. This is due to the fact that LOOCV utilizes a very small training set, as it leaves out only one observation for validation while using all remaining data for training. This approach can lead to a model that is overly optimistic, especially in cases where the data is limited or when the model has high variance, making it sensitive to variations in the training data. As a result, the estimates of test errors derived from LOOCV can be biased toward higher error rates because the model performs on unseen data that may not have been representative of the overall distribution.

In contrast, k-fold cross-validation, where the dataset is divided into k subsets, often provides a more robust estimate of the model's performance, as it avoids the issues encountered in LOOCV by averaging the error over multiple folds. The variance of k-fold cross-validation decreases as k increases, especially as it approaches the total number of observations (n), and this offers a more generalized estimate of the model's performance compared to the potentially skewed results from LOOCV.

The options discussing identical results and the variance characteristics of these methods also highlight differences between LOOCV