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...
Na minha lista:
| Main Authors: | , , |
|---|---|
| Formato: | Livro |
| Publicado em: |
Taylor & Francis Group,
2023-08-01T00:00:00Z.
|
| Assuntos: | |
| Acesso em linha: | Connect to this object online. |
| Tags: |
Adicionar Tag
Sem tags, seja o primeiro a adicionar uma tag!
|
| Resumo: | 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 infectious disease or cancer studies. This review examines the causes of overfitting and offers strategies to counteract it, focusing on model complexity reduction, reliable model evaluation, and harnessing data diversity. Through discussion of the underlying mathematical models and illustrative examples using both synthetic data and published real datasets, our objective is to equip analysts and bioinformaticians with the knowledge and tools necessary to detect and mitigate overfitting in their research. |
|---|---|
| Descrição do item: | 2164-5515 2164-554X 10.1080/21645515.2023.2251830 |