Resumen de Deducing neighborhoods of classes from a fitted classification model

Alexander Gerharz, Andreas Groll, Gunther Schauberger

• For complex statistical or machine learning models, interpretable machine learning methods can be used to make up for the lack of interpretability.

  The method proposed here helps to understand the partitioning of the feature space into predicted classes in a classification model. Basically, it observes the changes of the predictions after slight manipulations of specific metric features.

  The observed changes can then be interpreted as neighboring classes in the feature space. An example is shown with the iris classification task.