Search papers, labs, and topics across Lattice.
Competence Center for Clinical Trials Bremen, Leibniz Institute for Prevention Research and Epidemiology -BIPS, University of Bremen
4
0
4
A controlled deviation from traditional models allows for flexible, interpretable hybrid models that can learn complex interactions without losing clarity.
Unlock robust feature importance analysis with `xplainfi`, an R package that fills critical gaps by offering conditional importance methods and statistical inference for diverse ML models.
Epidemiologists can leverage this practical guide, complete with R code, to navigate the complexities of applying machine learning to modern, high-dimensional health data.
Additive explanations of survival models fail because of their inherent non-additivity, but now there's a way to decompose feature interactions into time-dependent components to understand *when* and *why* they fail.
