Which resampling method is likely to overestimate the test error rate compared to the validation set approach?

Leave-one-out cross-validation is a resampling method where each observation in the dataset is used as a test set while the remaining observations serve as the training set. This process is repeated for each data point, making it very thorough since it evaluates the model on every single data point available.

However, this method can lead to an overestimation of the test error rate when compared to a traditional validation set approach. Since each training set is very similar to the overall dataset (with only one observation left out), the model can potentially overfit to the training data. As a result, the performance may look worse in validation because the model has not truly been exposed to enough variation in unseen data. The repeated testing on similar data points can cause inflated error rates since the model might not generalize well to new, unseen instances that differ more widely from the training instances.

In contrast, methods such as k-fold cross-validation or bootstrap resampling have mechanisms in place that expose the model to different subsets of data, which can lead to a more accurate estimation of the true test error rate by balancing the bias and variance in model performance assessments.