Yingzhen Zhou

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Opinions on Interpretable Machine Learning and 70 Summaries of Recent Papers

I finished this book (by 'finish', I mean read through Chapt 4 through Chapt 7, and read them three times). 

Here's suggestion and what I think: 

  1. If you are comfortable reading online, use [this link] to read the GitBook version. A few benefits: errors are adjusted by the author in time, new sections coming from time to time that are only available here in the online version, and lastly, dark-mode possible. 
  2. From the TOC you'd see the book is mainly about model-agnostic methods, it introduces most of the model-agnostic concepts that are well-received. The list from this post are mostly for CV or NLP problems. Because my area is to interpret NNs that are trained for tabular data, I find the book very useful.
  3. In the book, each section has a "Pros" and "Cons" of the corresponding method, gives links to the GitHub repo that implements the corresponding method, both R and Python. This is handy.
  4. The illustrations and figures are clear and overall everything's well-explained.
  5. Downside is, the gradient methods (saliency map), concept detection (TCAV) are not described in detail. I'd recommend reading papers on those specific topics. (Plus, I also noticed that the updates of these chapters were not written by the author of this book. This is understandable as those require people with difference expertise.