Explainable AI for Practitioners: Designing and Implementing Explainable ML Solutions
暫譯: 實用的可解釋人工智慧：設計與實作可解釋的機器學習解決方案

Most intermediate-level machine learning books usually focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance and the need to be able to explain why and how your ML model makes the predictions that it does.

This practical guide brings together the best-in-class techniques for model interpretability and explains model predictions in a hands-on approach. Experienced ML practitioners will be able to more easily apply these tools in their daily workflow.