iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios
Maximilian Muschalik Fabian Fumagalli Rohit Jagtani Barbara Hammer Eyke Hüllermeier
Post-hoc explanation techniques such as the well-established partial
dependence plot (PDP), which investigates feature dependencies, are used in
explainable artificial intelligence (XAI) to understand black-box machine
learning models. While many real-world applications require dynamic models that
constantly adapt over time and react to changes in the underlying distribution,
XAI, so far, has primarily considered static learning environments, where
models are trained in a batch mode and remain unchanged. We thus propose a
novel model-agnostic XAI framework called incremental PDP (iPDP) that extends
on the PDP to extract time-dependent feature effects in non-stationary learning
environments. We formally analyze iPDP and show that it approximates a
time-dependent variant of the PDP that properly reacts to real and virtual
concept drift. The time-sensitivity of iPDP is controlled by a single smoothing
parameter, which directly corresponds to the variance and the approximation
error of iPDP in a static learning environment. We illustrate the efficacy of
iPDP by showcasing an example application for drift detection and conducting
multiple experiments on real-world and synthetic data sets and streams.