> It means you can blindly let the NN do your thinking for you, and come out ahead. I'm assuming by "ahead" you mean score the best on some arbitrary metric?

> It means you can blindly let the NN do your thinking for you, and come out ahead. I'm assuming by "ahead" you mean score the best on some arbitrary metric? Doing this approach you will not come out "ahead" when your model runs into problems in production and you need to diagnosis and get it performing as soon as possible. You also won't come out "ahead" in 5 years when a magical matrix of weights is the core of your predictive modeling efforts and no one on the team really understands exactly what was being done but the output of that model is crucial to another team... except it doesn't seem to be working. You also won't come out "ahead" when an event like the pandemic strikes, and the assumed distribution of all of your data no longer holds but you still have to keep your teams performance up for the month. I've been in the top 100 globally on kaggle so I understand pushing a metric higher, I've also worked for 10+ years doing DS work where a minuscule bump in a metric is not worth all the associated cost of a complex model. There have been at least three times where I have walked into a company that was running some mess of a neural network and replaced it with a one or two parameter model (that took seconds not days to train) and gotten practically identical performance to a NN that was causing massive headaches to maintain, train and debug. One of the most important principles of engineering that seems frighteningly lost on many data scientists I've met is that simplicity is the goal, and we only take on complexity when absolutely necessary. There are certainly cases were NN are the best path and you should let a million parameters do the thinking for you, but there cases are relatively few and getting solid performance here requires serious experience and skill.