The relative data hungriness of unpenalized and penalized logistic regression and ensemble-based machine learning methods: the case of calibration

The relative data hungriness of unpenalized and penalized logistic regression and ensemble-based machine learning methods: the case of calibration

Abstract Background Machine learning methods are increasingly being used to predict clinical outcomes. Optimism is the difference in model performance between derivation and validation samples. The term “data hungriness” refers to the sample size needed for a modelling technique to generate a predic...

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Bibliographic Details
Main Authors: Peter C. Austin, Douglas S. Lee, Bo Wang
Format: Article
Language:English
Published: BMC 2024-11-01
Series:Diagnostic and Prognostic Research
Subjects:
Machine learning
Monte Carlo simulations
Data-generating process
Random forests
Logistic regression
Generalized boosting methods
Online Access:https://doi.org/10.1186/s41512-024-00179-z
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https://doi.org/10.1186/s41512-024-00179-z

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