<P> Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input - output pairs . It infers a 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 inductive bias ) . </P>
<P> Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input - output pairs . It infers a 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 inductive bias ) . </P>
machine learning task of inferring a function from labelled training data is known as
machine learning task of inferring a function from labelled training data is known as
Supervised learning
Supervised learning