Publication Name	World Journal of Gastroenterology
Manuscript ID	72755
Country	United States

Received	2021-10-26 20:31
Peer-Review Started	2021-10-26 20:33
First Decision by Editorial Office Director	2021-12-27 01:58
Return for Revision	2021-12-27 01:58
Revised	2021-12-29 22:54
Publication Fee Transferred
Second Decision by Editor	2022-01-13 05:31
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2022-01-14 06:17
Articles in Press	2022-01-14 06:17
Edit the Manuscript by Language Editor
Typeset the Manuscript	2022-01-24 02:50
Publish the Manuscript Online	2022-01-25 09:22

ISSN	1007-9327 (print) and 2219-2840 (online)
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: http://creativecommons.org/Licenses/by-nc/4.0/
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Category	Gastroenterology & Hepatology
Manuscript Type	Letter to the Editor
Article Title	Machine learning models and over-fitting considerations
Manuscript Source	Invited Manuscript
All Author List	Paris Charilaou and Robert Battat
ORCID	Author(s) ORCID Number Paris Charilaou http://orcid.org/0000-0002-5512-4225 Robert Battat http://orcid.org/0000-0002-7421-9764
Funding Agency and Grant Number
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
Key Words	Machine learning; Over-fitting; Cross-validation; Hyper-parameter tuning
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.
Publish Date	2022-01-25 09:22
Citation	Charilaou P, Battat R. Machine learning models and over-fitting considerations. World J Gastroenterol 2022; 28(5): 605-607
URL	https://www.wjgnet.com/1007-9327/full/v28/i5/605.htm
DOI	https://dx.doi.org/10.3748/wjg.v28.i5.605

Full Article (PDF)	WJG-28-605.pdf
Full Article (Word)	WJG-28-605.docx
Manuscript File	72755_Auto_Edited.docx
Answering Reviewers	72755-Answering reviewers.pdf
Audio Core Tip	72755-Audio core tip.mp3
Conflict-of-Interest Disclosure Form	72755-Conflict-of-interest statement.pdf
Copyright License Agreement	72755-Copyright license agreement.pdf
Peer-review Report	72755-Peer-review(s).pdf
Scientific Misconduct Check	72755-CrossCheck.png
Scientific Misconduct Check	72755-Bing-Gong ZM-2.png
Scientific Editor Work List	72755-Scientific editor work list.pdf
CrossCheck Report	72755-CrossCheck report.pdf