Название: Explainable AI for Practitioners: Designing and Implementing Explainable ML Solutions
Автор: Michael Munn, David Pitman
Издательство: O’Reilly Media, Inc.
Год: 2023
Страниц: 279
Язык: английский
Формат: True PDF, True EPUB/Retail Copy
Размер: 39.0 MB
Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does. Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. Experienced Machine Learning engineers and data scientists will learn hands-on how these techniques work so that you'll be able to apply these tools more easily in your daily workflow. When developing Machine Learning (ML) models, I am sure all of you have asked the questions: Oh, how did it get that right? or That’s weird, why would it predict that? As software engineers, our first instinct is to trace through the code to find the answers. Unfortunately, this does not get us very far with ML models because their “code” is automatically generated, not human-readable, and may span a vast number (sometimes billions!) of parameters. One needs a special set of tools to understand ML models. Explainable AI (XAI) is a field of Machine Learning focused on developing and analyzing such tools.