In supervised learning, an algorithm is trained with labeled training data in order to generate predictions or judgements. The input-output pairings that make up the training data are used by the algorithm to learn the mapping between inputs and matching outputs. By applying the patterns it discovered during training to new, unidentified data, the algorithm is able to generalize and generate predictions thanks to this learning process.
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