Interpretable Machine Learning with iml and mlr

Machine learning models repeatedly outperform interpretable, parametric models like the linear regression model. The gains in performance have a price: The models operate as black boxes which are not interpretable. Fortunately, there are many methods that can make machine learning models interpretable. Feature importance: Which were the most important features? Feature effects: How does a feature influence the prediction?