Editing Supervised learning (section)

{{Short description|Machine learning paradigm}}
[[File:Supervised_and_unsupervised_learning.png|thumb|upright=1.4|In supervised learning, the training data is labeled with the expected answers, while in [[unsupervised learning]], the model identifies patterns or structures in unlabeled data.]]
In [[machine learning]], '''supervised learning''' ('''SL''') is a paradigm where a [[Statistical model|model]] is trained using input objects (e.g. a vector of predictor variables) and desired output values (also known as a ''supervisory signal''), which are often human-made labels. The training process builds a function that maps new data to expected output values.<ref>[[Mehryar Mohri]], Afshin Rostamizadeh, Ameet Talwalkar (2012) ''Foundations of Machine Learning'', The MIT Press {{ISBN|9780262018258}}.</ref> An optimal scenario will allow for the algorithm to accurately determine output values for unseen instances. This requires the learning algorithm to [[Generalization (learning)|generalize]] from the training data to unseen situations in a reasonable way (see [[inductive bias]]). This statistical quality of an algorithm is measured via a ''[[generalization error]]''.