Supervised Learning
Supervised learning is a class of machine learning that learns to map inputs to outputs from labelled training examples. Given enough examples of inputs paired with known correct answers, it predicts the answer for new inputs.
Supervised learning covers both classification (predicting a category, such as fault type) and regression (predicting a number, such as remaining useful life). Its accuracy depends heavily on the quantity and quality of labelled data, which in industry often means months of records tagged with confirmed outcomes. When good labels exist it is powerful and interpretable; when they do not, unsupervised or semi-supervised methods are used instead.