  Model-agnostic tools for interpreting machine-learning models struggle to
summarize the joint effects of strongly dependent features in high-dimensional
feature spaces, which play an important role in pattern recognition, for
example in remote sensing of landcover. This contribution proposes a novel
approach that interprets machine-learning models through the lens of feature
space transformations. It can be used to enhance unconditional as well as
conditional post-hoc diagnostic tools including partial dependence plots,
accumulated local effects plots, or permutation feature importance assessments.
While the approach can also be applied to nonlinear transformations, we focus
on linear ones, including principal component analysis (PCA) and a partial
orthogonalization technique. Structured PCA and diagnostics along paths offer
opportunities for representing domain knowledge. The new approach is
implemented in the R package `wiml`, which can be combined with existing
explainable machine-learning packages. A case study on remote-sensing landcover
classification with 46 features is used to demonstrate the potential of the
proposed approach for model interpretation by domain experts.
