Frame Supervised Learning as Labeled Prediction
Identify when a business problem is a supervised-learning problem and explain how labels, training, and inference connect.
The reframe: supervised learning is not "smart automation." It is labeled prediction. Start with the answer field A supervised model needs examples that already contain the outcome you care about. Each row must pair features with a label. Without that answer field, the model has nothing to compare itself against during training. Separate training from inference Training is the learning phase: the model sees labeled examples, makes predictions, measures error, and adjusts. Inference is the serving phase: the trained model sees only features on a new case and outputs a prediction. Confusing those phases causes bad project scoping. Audit the…
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