⇒ The efficiency of how quickly you can get a machine learning system to work well, will depend to a large part on how well you can repeatedly make good decisions about what to do next in the course of a machine learning project.
⇒ On any given machine learning application, it will often turn out that some of these things could be fruitful, and some of these things not fruitful. The key to being effective at how you build a machine learning algorithm will be if you can find a way to make good choices about where to invest your time. For example, I have seen teams spend literally many many months collecting more training examples, thinking that more training data is going to help, but it turns out sometimes it helps a lot, and sometimes it doesn't.
A diagnostic is a test that you run to gain insight into what is/isn’t working with a learning algorithm, to gain guidance into improving its performance.
