Thanks for the write-up! The "empathize with the model" line is great. :) I think the problem is really that people (in ML especially) come up with solutions b

Thanks for the write-up! The "empathize with the model" line is great. :) I think the problem is really that people (in ML especially) come up with solutions before they've properly framed the problem that the solution is trying to solve. We should be finding solutions to actual problems, observed by looking at the data and past model performance on that data. And once we've got a good problem, there's usually more than one way to address it. Your initial 'good idea' is typically just one, and often not the easiest to implement or evaluate. So, when faced with an observable problem, I formulate a number of possible causes (hypotheses), and then list two or three ways that I might solve the problem if that hypothesis is correct. I then pick the /simplest/ solution for each hypothesis, implement them, and see how each one does. Some will have more effect than others; this helps update my prior on which hypothesis might really be driving the problem, at which point I might consider revisting some of the more complicated solutions. Assuming it hasn't already been fixed!