Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which is called a classifier (if the output is discrete; see classification) or a regression function (if the output is continuous; see regression). The inferred function should predict the correct output value for any valid input object. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias).
The parallel task in human and animal psychology is often referred to as concept learning.
Also see unsupervised learning.
Read more about Supervised Learning: Overview, How Supervised Learning Algorithms Work, Generative Training, Generalizations of Supervised Learning, Approaches and Algorithms, Applications, General Issues
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