Marvin N. Wright

This framework introduces kernel-based output representations that enable time-dependency-aware explanations at multiple levels of temporal granularity, including time-specific, time-resolved, and time-aggregated, while providing a unified view in which existing methods arise as special cases.

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.