The same cave wants one model that stamps each wheel “sell / hold / discard” and another that estimates days-to-peak in a decimal. Both use labeled history. How should that supervised work be split?

Inside supervised learning, classification uses predefined classes and ML regression uses continuous numeric targets. Treating the decimal as a class, reading “regression” as Foundation-level retest, or calling labeled work unsupervised is incorrect.