[e_ameisen]

Yes, this is the approach in the book. The concept of question quality is nuanced, and does not have a clear definition. It can be easy to feel like you've solved the problem by just throwing in ML and calling it a day, but producing something useful is a real challenge.

The book covers multiple aspect of that process, from choosing an ML approach that isn't too simple or ambitious, to iterating on a model within the context of its final use case (i.e rather than only optimizing for a metric, testing how the model helps with its end goal).

In my experience, I've found that it is often those challenges that make or break the quality of an ML product, so the book focuses on tools to make complex problems more tractable, and less risky.