It-tagħlim sorveljat itrawwem mudell fuq eżempji etikettati (inputs ippareati mal-outputs korretti) sabiex jitgħallem jipprediċi outputs għal inputs ġodda. Huwa l-iktar tip komuni ta' ML, użat għall-klassifikazzjoni u regressjoni. Il-fehim tiegħu jipprofonda l-għarfien ta' ML.
Kif taħdem it-tagħlim sorveljat
TRAIN on LABELED data (input → known correct output):
1. collect a DATASET of examples with labels (e.g. emails labeled spam/not-spam)
2. split into TRAINING and TEST sets
3. the model learns to map inputs → outputs by minimizing prediction error on training data
4. EVALUATE on the test set (unseen data) → measure how well it generalizes
5. use the trained model to PREDICT outputs for new inputs (inference)
→ learn from examples with answers → predict answers for new cases
