What cross-validation method is defined by leaving one participant out to minimize performance issues?

The method defined by leaving one participant out for evaluation is known as Leave-One-Out Cross-Validation (LOOCV). In this technique, the dataset is partitioned such that one instance is held out as the test set while the remaining instances are used for training. This process is repeated for each instance in the dataset, allowing every data point to be used for both training and testing.

LOOCV is particularly beneficial when the available dataset is small, as it maximizes the training data used for each iteration and provides a detailed performance measure. By evaluating the model on each individual instance, LOOCV helps to address potential performance issues that may arise from a limited sample size, leading to more reliable and robust validation results.

Other methods mentioned serve different purposes; for instance, bootstrapping involves sampling with replacement and is typically used for estimating the distribution of a statistic. K-Fold Cross-Validation subdivides the dataset into K separate folds, where each fold is used as the test set in turn, but does not leave out a single instance as LOOCV does. Stratified sampling is a technique related to sampling that ensures that different categories within a dataset are properly represented, but does not necessarily focus on the training/testing split method like LO