Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls

Abstract

Machine learning models developed with these methodological pitfalls, which are undetectable during internal evaluation, produce inaccurate predictions; thus, understanding and avoiding these pitfalls is necessary for developing generalizable models.<b>Keywords:</b> Random Forest, Diagnosis, Prognosis, Convolutional Neural Network (CNN), Medical Image Analysis, Generalizability, Machine Learning, Deep Learning, Model Evaluation <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license.