Machine learning models and over-fitting considerations  

作　　者：Paris Charilaou Robert Battat 

机构地区：[1]Jill Roberts Center for Inflammatory Bowel Disease-Division of Gastroenterology&Hepatology,Weill Cornell Medicine,New York,NY 10021,United States

出　　处：《World Journal of Gastroenterology》2022年第5期605-607,共3页世界胃肠病学杂志（英文版）

摘　　要：Machine learning models may outperform traditional statistical regression algorithms for predicting clinical outcomes.Proper validation of building such models and tuning their underlying algorithms is necessary to avoid over-fitting and poor generalizability,which smaller datasets can be more prone to.In an effort to educate readers interested in artificial intelligence and model-building based on machine-learning algorithms,we outline important details on crossvalidation techniques that can enhance the performance and generalizability of such models.

关 键 词：Machine learning OVER-FITTING Cross-validation Hyper-parameter tuning 

分 类 号：R318[医药卫生—生物医学工程]