Nested cross-validation when selecting classifiers is overzealous for most practical applications

When selecting a classification algorithm to be applied to a particular problem, one has to simultaneously select the best algorithm for that dataset and the best set of hyperparameters for the chosen model. The usual approach is to apply a nested cross-validation procedure: hyperparameter selection is performed in the inner cross-validation, while the outer cross-validation computes an unbiased estimate of the expected accuracy of the algorithm with cross-validation based hyperparameter tuning. The alternative approach, which we shall call flat cross-validation, uses a single cross-validation step both to select the optimal hyperparameter values and to provide an estimate of the expected accuracy of the algorithm that, while biased, may nevertheless still be used to select the best learning algorithm. We tested both procedures using 12 different algorithms on 115 real-life binary datasets and conclude that using the less computationally costly flat cross-validation procedure will generally result in the selection of an algorithm that is, for all practical purposes, of similar quality to that selected via nested cross-validation, provided the learning algorithms have relatively few hyperparameters to be optimised.

Automated summary

When selecting a classification algorithm for a specific problem, it is crucial to choose the best set of hyperparameters for the dataset and model. The study compares nested cross-validation and flat cross-validation approaches using 12 algorithms on 115 real-life binary datasets, finding that the flat cross-validation procedure, despite being biased, generally leads to similar algorithm selection results but with lower computational costs.