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One of the most popular approaches to understanding feature effects of modernblack box machine learning models are partial dependence plots (PDP). Theseplots are easy to understand but only able to visualize low order dependencies.The paper is about the question 'How much can we see?': A framework isdeveloped to quantify the explainability of arbitrary machine learning models,i.e. up to what degree the visualization as given by a PDP is able to explainthe predictions of the model. The result allows for a judgement whether anattempt to explain a black box model is sufficient or not.

How Much Can We See? A Note on Quantifying Explainability of Machine Learning Models