Supervised learning

In machine learning, supervised learning is the task of inferring a function from labelled training data. The results of the training are known beforehand, the system simply learns how to get to these results correctly. Usually, such systems work with vectors. They get the training data and the result of the training as two vectors and produce a classifier. Usually, the system uses inductive reasoning to generalize the training data.

Supervised Learning Media

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  In supervised learning, the training data is labeled with the expected answers, while in unsupervised learning, the model identifies patterns or structures in unlabeled data.

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  Tendency for a task to employ supervised vs. unsupervised methods. Task names straddling circle boundaries is intentional. It shows that the classical division of imaginative tasks (left) employing unsupervised methods is blurred in today's learning schemes.