Supervised Learning

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Supervised Learning is a machine learning technique to learn a function that maps an input to an output based on example input-output pairs. It infers the function from labeled training data consisting 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 can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a “reasonable” way. See also Unsupervised Learning.

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