A machine learning approach defined by its use of labeled datasets to train algorithms that classify data or predict outcomes accurately.
Supervised Learning underlies most traditional enterprise AI. Trained on curated, human-labeled examples (for instance, fraudulent and valid transactions), the model learns a decision boundary that separates the categories and can then classify new cases automatically.
See also Large Language Model, Fine-Tuning and Model Inference.