Data Science, Machine Learning, Model Selection, Validation, Modeling.
on the validation set. Cross-validation: Cross-validation involves splitting the data into k-folds, where k is the number of folds. The model is trained on k-1 folds and evaluated on the remaining fold. This process is repeated k times, with each fold used as the validation set once. Leave-one-out