Classification learning; Concept learning; Learning with a teacher; Statistical decision techniques; Supervised learning
In Classification learning, an algorithm is presented with a set of classified examples or “instances” from which it is expected to infer a way of classifying unseen instances into one of several “classes”. Instances have a set of features or “attributes” whose values define that particular instance. Numeric prediction, or “regression,” is a variant of classification learning in which the class attribute is numeric rather than categorical. Classification learning is sometimes called supervised because the method operates under supervision by being provided with the actual outcome for each of the training instances. This contrasts with clustering where the classes are not given, and with association learning which seeks any association – not just one that predicts the class.
Classification learning grew out of two strands of...
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