ml-problem-framing

Decide whether, and how, machine learning should solve a business problem before any data work starts. Use when someone asks whether ML or AI can help with a problem; wants to predict, classify, forecast, rank, recommend, detect anomalies or group things but hasn't pinned down how; needs to choose between rules and a model; must define the target or label, or find a proxy label when true outcomes are missing; wants to pick the success metric and tie it to business value; or needs to judge whether an ML project is feasible and worth funding. Not for a dataset that is already chosen and ready for cleaning, feature building, model tuning or threshold setting.