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Interpretation of machine learning models has become one of the mostimportant research topics due to the necessity of maintaining control andavoiding bias in these algorithms. Since many machine learning algorithms arepublished every day, there is a need for novel model-agnostic interpretationapproaches that could be used to interpret a great variety of algorithms. Thus,one advantageous way to interpret machine learning models is to feed differentinput data to understand the changes in the prediction. Using such an approach,practitioners can define relations among data patterns and a model's decision.This work proposes a model-agnostic interpretation approach that usesvisualization of feature perturbations induced by the PSO algorithm. Wevalidate our approach on publicly available datasets, showing the capability toenhance the interpretation of different classifiers while yielding very stableresults compared with state-of-the-art algorithms.

Model-agnostic interpretation by visualization of feature perturbations