Predictive overfitting in immunological applications: Pitfalls and solutions
Overfitting describes the phenomenon where a highly predictive model on the training data generalizes poorly to future observations. It is a common concern when applying machine learning techniques to contemporary medical applications, such as predicting vaccination response and disease status in in...
Saved in:
Main Authors: | Jeremy P. Gygi (Author), Steven H. Kleinstein (Author), Leying Guan (Author) |
---|---|
Format: | Book |
Published: |
Taylor & Francis Group,
2023-08-01T00:00:00Z.
|
Subjects: | |
Online Access: | Connect to this object online. |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions
by: Yu-Qing Cai, et al.
Published: (2024) -
Mapping snakebite epidemiology in Nicaragua--pitfalls and possible solutions.
by: Erik Hansson, et al.
Published: (2010) -
Pitfalls and solutions of the fully-automated radiosynthesis of [11C]metoclopramide
by: Verena Pichler, et al.
Published: (2019) -
Challenges and Pitfalls for Implementing Digital Health Solutions in Clinical Studies in Europe
by: Marcel Meyerheim, et al.
Published: (2021) -
In Silico Veritas: The Pitfalls and Challenges of Predicting GPCR-Ligand Interactions
by: Sander B. Nabuurs, et al.
Published: (2011)