In supervised learning an AI learns from examples that already include the correct answer.
Example
A spam filter learns from thousands of emails that people marked as “spam” or “not spam”.
For pros
Training data consists of an input and a label; the model learns the mapping. The counterparts are unsupervised learning (no labels) and self-supervised learning, which is how language models are pretrained. Quality depends heavily on clean, balanced labels.
