Machine Learning

Introduction What Kinds of explanation does CIU generate? How does CIU work? A toy example: predicting breast Permutation feature importance Decision Tree Classifier Random Forest Classifier Gradient Boosting Classifier Explaining a single observation Generating Textual Explanations Drawbacks Introduction Explainability is a hot topic in the machine learning research community these days. Over the past few years, many methods have been introduced to understand how a machine learning model makes a prediction.

Last September, I had the opportunity to participate in the EXPLAINABLE DATA SCIENCE summer school in Kirchberg, Luxembourg. the summer school was organized by the European Association for Data Science (EuADS) and was held during 10-13 September. What I specifically liked about this summer school ( of course besides enjoying the the beautiful city of Luxembourg ) was the fact that it covered a vast variety of topics in the explainable machine learning (AI) literature, ranging from visualization, XAI techniques, causality to psychological aspects of explainability.

In this paper we reviewed the recent literature on the use of machine learning in rail track maintenance.

In this paper we reviewed the recent literature on the use of machine learning in rail track maintenance.