Letter to the Editor
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Feb 7, 2022; 28(5): 605-607
Published online Feb 7, 2022. doi: 10.3748/wjg.v28.i5.605
Machine learning models and over-fitting considerations
Paris Charilaou, Robert Battat
Paris Charilaou, Robert Battat, Jill Roberts Center for Inflammatory Bowel Disease - Division of Gastroenterology & Hepatology, Weill Cornell Medicine, New York, NY 10021, United States
Author contributions: Charilaou P and Battat R drafted and edited the manuscript, and reviewed the intellectual content.
Conflict-of-interest statement: The authors have no conflict of interest to declare.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Robert Battat, MD, Assistant Professor, Jill Roberts Center for Inflammatory Bowel Disease - Division of Gastroenterology & Hepatology, Weill Cornell Medicine, 1315 York Avenue, New York, NY 10021, United States. rob9175@med.cornell.edu
Received: October 26, 2021
Peer-review started: October 26, 2021
First decision: December 27, 2021
Revised: December 29, 2021
Accepted: January 14, 2022
Article in press: January 14, 2022
Published online: February 7, 2022
Processing time: 90 Days and 12.8 Hours
Core Tip

Core Tip: Machine learning models are increasingly being used in clinical medicine to predict outcomes. Proper validation techniques of these models are essential to avoid over-fitting and poor generalization on new data.