Machine Learning Interpretability: Explaining Blackbox Models with LIME

This is the second part of our series about Machine Learning interpretability. We want to describe LIME (Local Interpretable Model-Agnostic Explanations), a popular technique to explain blackbox models. It was proposed by Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin in their paper Why Should I Trust You? Explaining the Predictions of Any Classifier, which they first presented at the ACM's Conference on Knowledge Discovery and Data Mining in 2016. Please check out our previous article if you are not familiar with the concept of interpretability. We previously made a distinction between model-specific and model-agnostic techniques as well as between global and local techniques.