(untitled) (Informatica, 2018)
State-of-the-art prediction models are getting increasingly complex and incomprehensible for humans. This is problematic for many application areas, especially those where knowledge discovery is just as important as predictive performance, for example medicine or business consulting. As machine learning and artificial intelligence are playing an increasingly large role in the society through data based decision making, this is problematic also from broader perspective and worries general public as well as legislators. As a possible solution, several explanation methods have been recently proposed, which can explain predictions of otherwise opaque models. These methods can be divided into two main approaches: gradient based approaches limited to neural networks, and more general perturbation based approaches, which can be used with arbitrary prediction models. We present an overview of perturbation based approaches, and focus on a recently introduced implementation of two successful methods developed in Slovenia, EXPLAIN and IME. We first describe their working principles and visualizations of explanations, followed by the implementation in ExplainPrediction package for R environment.