The bit about choosing good loss functions is a nice general point that I find is ignored in many machine learning set ups: if you're going to work so hard making a model optimize some objective, make sure it's the right objective!
Totally. It often becomes a trade-off of doing an alright job, e.g. approximately, optimizing the loss you're really interested in vs. doing a great job optimizing a tangentially related but much less appropriate loss.
3 comments
[ 2.7 ms ] story [ 12.3 ms ] thread