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Decide what the model is supposed to predict, for whom or what, and at what moment. You’ll distinguish supervised learning tasks from other uses of data, and tell classification questions from regression questions.
Turn past cases into examples with input features and a known label. You’ll practice choosing features that would truly be available at prediction time and spotting target leakage, where the answer accidentally sneaks into the inputs.
Apply the previous explanations in a guided problem.
Trace how training data becomes a fitted model: examples go in, the model tries a pattern, errors create a learning signal, and the final rule is saved for later use. You’ll separate machine-learned patterns from hand-written rules and memorization.
Run a new case through the same feature recipe and read the model’s prediction as an output. You’ll reason through why the label is unknown at prediction time and why training and prediction must use matching inputs.
Check your understanding with a short quiz.
Compare predictions with true labels on examples the model did not train on. You’ll use the ideas behind training, validation, and test sets to see why low training error can still fail on new cases.
Follow the loop after a prediction is used: outcomes arrive later, new labels become feedback, and performance can change as the world changes. You’ll recognize when to collect better data, retrain, or question whether the prediction is still useful.
Review this chapter with practice based on your mistakes.
