  A fundamental question on the use of ML models concerns the explanation of
their predictions for increasing transparency in decision-making. Although
several interpretability methods have emerged, some gaps regarding the
reliability of their explanations have been identified. For instance, most
methods are unstable (meaning that they give very different explanations with
small changes in the data), and do not cope well with irrelevant features (that
is, features not related to the label). This article introduces two new
interpretability methods, namely VarImp and SupClus, that overcome these issues
by using local regressions fits with a weighted distance that takes into
account variable importance. Whereas VarImp generates explanations for each
instance and can be applied to datasets with more complex relationships,
SupClus interprets clusters of instances with similar explanations and can be
applied to simpler datasets where clusters can be found. We compare our methods
with state-of-the art approaches and show that it yields better explanations
according to several metrics, particularly in high-dimensional problems with
irrelevant features, as well as when the relationship between features and
target is non-linear.
