Machine Learning Explainability – Towards Data Science

Recently, I did the micro course Machine Learning Explainability on kaggle.com. I can highly recommend this course as I have learned a lot of useful methods to analyse a trained ML model. For a brief overview of the topics covered, this blog post will summarize my learnings. The following paragraphs will explain the methods Permutation Importance, Partial Dependence Plots and SHAP Values. I will illustrate the methods using the famous Titanic dataset.