Examples include emails labelled spam or not, X-rays labelled with diagnoses or houses with sale prices. The model learns a mapping from inputs to labels and is judged on new examples it hasn’t seen.
Labelled data is often the bottleneck, which is why self-supervised pretraining on unlabelled data became so important.