The Harms of Class Imbalance Corrections for Machine Learning Based Prediction Models: A Simulation Study.

ABSTRACT:

Introduction

Risk prediction models are increasingly used in healthcare to aid in clinical decision-making. In most clinical contexts, model calibration (i.e., assessing the reliability of risk estimates) is critical. Data available for model development are often not perfectly balanced with the modeled outcome (i.e., individuals with vs. without the event of interest are not equally prevalent in the data). It is common for researchers to correct for class imbalance, yet, the effect of such imbalance corrections on the calibration of machine learning models is largely unknown.

Methods

We studied the effect of imbalance corrections on model calibration for a variety of machine learning algorithms. Using extensive Monte Carlo simulations we compared the out-of-sample predictiv

SUBMITTER: Carriero A 

PROVIDER: S-EPMC11771573 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature