What k-fold cross-validation method uses all data points in the dataset as folds?

The correct answer is LOOCV, which stands for Leave-One-Out Cross-Validation. This method involves using all but one data point in the dataset as the training set and the single remaining data point as the validation set. This process is repeated so that each data point in the dataset serves as the validation set once, effectively creating as many folds as there are data points in the dataset.

LOOCV is particularly beneficial in situations where the dataset is small, as it maximizes the training data for each iteration, thus providing a better understanding of the model's performance by making full use of the available data. This results in a very reliable estimation of the model's predictive performance.

In contrast, bootstrap sampling typically involves resampling data with replacement to create multiple training sets and leave out various portions of the data without a fixed fold approach. Stratified K-Fold is a method that divides the dataset into k folds, ensuring that each fold has a representative distribution of the target variable, but it does not use every data point as a fold. LPOCV (Leave-P-Out Cross-Validation) is another variation like LOOCV but removes p data points at a time for validation, rather than just one, thus it does not utilize all data