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Supervised classification is the result of supervised learning; this term refers to the automatic learning methodology in which the machine is given examples made up of a pair of data containing the original data and the expected result. The task of the machine is to find the rule (function or model) with which to create a relationship between the two in such a way that, when a previously unknown example occurs, it can obtain the correct result.
How does it work
The data, which will be passed on to artificial intelligence, must be previously labeled, i.e. assigned to a certain category.
The algorithm analyzes the sample data and derives a general rule thanks to which, when a new unlabeled case arises, it will be able to classify it.
On the basis of the model that the algorithm has created, he will be able to classify the data that will be provided respecting the expected results.
When to choose it
Supervised learning is mainly used for classification problems, such as in marketing to classify potential customers and propose products they might be interested in on the basis of the profile and purchase history. Another example are email anti-spam systems which, upon receipt of a message, are able to decide whether a particular email should be labeled as spam or not.