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

TL;DR

This paper compares nested and flat cross-validation methods for classifier selection, finding that flat cross-validation is often sufficient and more practical when hyperparameters are few, reducing computational costs without sacrificing much accuracy.

Contribution

It demonstrates that flat cross-validation can replace nested cross-validation in many practical scenarios, simplifying the model selection process.

Findings

Flat cross-validation yields similar classifier quality as nested cross-validation.

Using flat cross-validation reduces computational costs significantly.

The approach is effective when algorithms have few hyperparameters.

Abstract

When selecting a classification algorithm to be applied to a particular problem, one has to simultaneously select the best algorithm for that dataset \emph{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 \emph{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 expensive 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.