tag: #classification

Small classifiers swing by several points of accuracy between training runs, purely from nondeterminism. Most people train once and ship whatever they got. Training K seeds and selecting is the fix, but selection is where people quietly cheat by maximizing accuracy. The discipline is to pick one metric you refuse to compromise on, gate on it, and compute that gate as the minimum across every eval set you have.