A bias correction for the minimum error rate in cross-validation

TL;DR

This paper introduces a simple bias correction method for the minimum error rate in cross-validation, improving the accuracy of test error estimates without significant additional computation.

Contribution

It proposes a novel bias estimation technique for cross-validation error minima, enhancing model tuning reliability in supervised learning.

Findings

Bias correction improves test error estimation accuracy.

Method performs well across various classifiers and settings.

Requires minimal additional computational effort.

Abstract

Tuning parameters in supervised learning problems are often estimated by cross-validation. The minimum value of the cross-validation error can be biased downward as an estimate of the test error at that same value of the tuning parameter. We propose a simple method for the estimation of this bias that uses information from the cross-validation process. As a result, it requires essentially no additional computation. We apply our bias estimate to a number of popular classifiers in various settings, and examine its performance.