Machine-Learning VS DiCE

Thank you for sharing! Am I the only one who never learned tu-ples, lists, dictionaries, arrays and so on yet able to write some rather sophisticated Python code without really understanding the data structures that I use? See my GitHub repository at https://github.com/VincentGranville/Machine-Learning, full of Python code. I play with data structures the same way I play with grammar in English: I do it successfully, without knowing the rules or the inner workings.

Top entries are in bold, and sub-entries are in italics. This dictionary is from my new book “Intuitive Machine Learning and Explainable AI”, available here and used as reference material for the course with the same name (see here). These entries are cross-referenced in the book to facilitate navigation, with backlinks to the pages where they appear. The index, also with clickable backlinks, is a more comprehensive listing with 300+ terms. Both the glossary and index are available in PDF format here on my GitHub repository, and of course with clickable links within the book.

- all domains requiring high interpretability absolutely ignore deep learning at all, and put all their research into traditional ML; see e.g. counterfactual examples, important interpretability methods in finance, or rule-based learning, important in medical or law applications

Counter-factual and recourse-based explanations are alternative approach to model explanations. I used to work in a large financial institution, and we were researching whether counter-factual explanation methods would lead to better reason codes for adverse action notices.