This was a really interesting and insightful comment, thanks for sharing. I think the conclusion I shared in my sibling comment was probably a little too broad.
I particularly like this:
> In my experience it really comes down to how many “tricks” you know for each algorithm and how well can you apply and combine these “tricks”. The difference is that neural networks have many more of these tricks and a broader coverage of research detailing the interactions between them.
This is pretty true - the lack of knobs to turn on something like XGBoost or LightGBM both make it pretty easy to get good results and harder to fine tune results for your specific problem. Maybe this isn't the most correct way to look at it, but I've always sort of pictured it as curve where you are plotting effort vs results, and the one for LightGBM/XGBoost starts out higher but is more flat, and the NN one is steeper.
I guess reading your post makes me wonder where the two curves cross? Do you have good intuition for that, or do you feel so comfortable with neural networks that they are sort of your default? I peeked at the company you have listed in your bio, and it looks like you have pretty deep experience with neural networks and work with other people who have been in research roles in that area too, and I wonder how that changes your curve compared to the average ML practitioner? Certainly figuring out how to pick the best layer combinations, optimizer, loss functions, etc benefits hugely from intuition gained over years of experience.
I think your conclusions are accurate. For many problems LightGBM or xgboost can often yield decent results in short amounts of time and for many problems that’s sufficient. A lot of the work we do is about pushing the results as far as we can take them and the business case justifies the extra time it can take to get there. For those types of problems, today, we would probably choose a neural network because then we have a lot more knobs as you mentioned.
Just like the rest of ML, whether neural networks are the right choice still depends on the problem at hand and the team implementing the solution. It definitely impacts where the performance / time curves intersect. If we just need something decent fast, or we’re working with another team that doesn’t have the same background, we tend to focus on approaches with fewer moving pieces. If we need the best possible performance, have a qualified team to get there, and have the time to iterate on development then the curves would favor neural networks.
I particularly like this:
> In my experience it really comes down to how many “tricks” you know for each algorithm and how well can you apply and combine these “tricks”. The difference is that neural networks have many more of these tricks and a broader coverage of research detailing the interactions between them.
This is pretty true - the lack of knobs to turn on something like XGBoost or LightGBM both make it pretty easy to get good results and harder to fine tune results for your specific problem. Maybe this isn't the most correct way to look at it, but I've always sort of pictured it as curve where you are plotting effort vs results, and the one for LightGBM/XGBoost starts out higher but is more flat, and the NN one is steeper.
I guess reading your post makes me wonder where the two curves cross? Do you have good intuition for that, or do you feel so comfortable with neural networks that they are sort of your default? I peeked at the company you have listed in your bio, and it looks like you have pretty deep experience with neural networks and work with other people who have been in research roles in that area too, and I wonder how that changes your curve compared to the average ML practitioner? Certainly figuring out how to pick the best layer combinations, optimizer, loss functions, etc benefits hugely from intuition gained over years of experience.