Comparing variable and feature selection strategies for prediction - protocol of a simulation study in low-dimensional transplantation data.

ABSTRACT: The integration of machine learning methodologies has become prevalent in the development of clinical prediction models, often suggesting superior performance compared to traditional statistical techniques. Within the scope of low-dimensional datasets, encompassing both classical and machine learning paradigms, we plan to undertake a comparison of variable selection methodologies through simulation-based analysis. The principal aim is the comparison of the variable selection strategies with respect to relative predictive accuracy and its variability, with a secondary aim the comparison of descriptive accuracy. We use six distinct statistical learning approaches across both data generation and model learning. The present manuscript is a protocol for the corresponding simulation study regist

SUBMITTER: Hoessly L 

PROVIDER: S-EPMC12316309 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature