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Comparing penalization methods for linear models on large observational health data | Synapse

May 20, 2024Open Access

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Comparing penalization methods for linear models on large observational health data

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Abstract

This study evaluates regularization variants in logistic regression (L1, L2, ElasticNet, Adaptive L1, Adaptive ElasticNet, Broken adaptive ridge BAR, and Iterative hard thresholding IHT) for discrimination and calibration performance, focusing on both internal and external validation.

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Cite This Study

Friðgeirsson et al. (Mon,) studied this question.

synapsesocial.com/papers/68e69377b6db64358761a789 https://doi.org/https://doi.org/10.1093/jamia/ocae109

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