Package: xplaineff 0.1.1

xplaineff: Decomposing Global Feature Effects Based on Feature Interactions

Implements the GADGET (Generalized Additive Decomposition of Global EffecTs) algorithm for interpretable machine learning. The package recursively partitions the feature space to minimize heterogeneity of feature effects (e.g., Accumulated Local Effects or Partial Dependence), producing a tree of regions where effects are more stable. It supports both ALE and PD strategies, works with 'mlr3' learners and provides visualization of the interaction tree and regional effect plots. The method is described in Herbinger, J., Wright, M. N., Nagler, T., Bischl, B., and Casalicchio, G. (2024), "Decomposing Global Feature Effects Based on Feature Interactions" <https://jmlr.org/papers/volume25/23-0699/23-0699.pdf>.

Authors:Zizheng Zhang [aut, cre], Coco Bögel [aut], Giuseppe Casalicchio [aut], Bernd Bischl [aut]