Regionally Additive Models: Explainable-by-design models minimizing
  feature interactions
Vasilis Gkolemis Anargiros Tzerefos Theodore Dalamagas Eirini Ntoutsi Christos Diou
Generalized Additive Models (GAMs) are widely used explainable-by-design
models in various applications. GAMs assume that the output can be represented
as a sum of univariate functions, referred to as components. However, this
assumption fails in ML problems where the output depends on multiple features
simultaneously. In these cases, GAMs fail to capture the interaction terms of
the underlying function, leading to subpar accuracy. To (partially) address
this issue, we propose Regionally Additive Models (RAMs), a novel class of
explainable-by-design models. RAMs identify subregions within the feature space
where interactions are minimized. Within these regions, it is more accurate to
express the output as a sum of univariate functions (components). Consequently,
RAMs fit one component per subregion of each feature instead of one component
per feature. This approach yields a more expressive model compared to GAMs
while retaining interpretability. The RAM framework consists of three steps.
Firstly, we train a black-box model. Secondly, using Regional Effect Plots, we
identify subregions where the black-box model exhibits near-local additivity.
Lastly, we fit a GAM component for each identified subregion. We validate the
effectiveness of RAMs through experiments on both synthetic and real-world
datasets. The results confirm that RAMs offer improved expressiveness compared
to GAMs while maintaining interpretability.