Experiment Design for ML

A single split is always sufficient if the test set is large enough A single split can produce results that are artifacts of the particular partition; cross-validation with statistical significance testing gives more reliable model comparisons Multiple splits are only needed for small datasets with fewer than 100 samples Single splits are preferred because cross-validation takes too long