supervised learning workflows

Supervised learning is the model workflow for repeated decisions with labeled examples. This chapter keeps the loop practical: choose a label, keep features separate from the answer, hold back review examples, compare to a baseline, and inspect prediction receipts before trusting the result.

The examples stay close to workplace tools: routing support tickets, flagging lead follow-up, labeling research notes, and checking risky handoffs. The goal is builder literacy, not an ML textbook. You should leave able to tell when a small classifier is worth trying and when a rule, API call, search tool, or checklist is the better first artifact.

By the end, you can shape a supervised-learning brief with a baseline, leakage check, train/test receipt, prediction receipt, and acceptance criteria a teammate can review.