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Supervised learning is a method used to enable machines to classify objects, problems or situations based on related data fed into the machines. Machines are fed with data such as characteristics, patterns, dimensions, color and height of objects, people or situations repetitively until the machines are able to perform accurate classifications. Supervised learning is a popular technology or concept that is applied to real-life scenarios. Supervised learning is used to provide product recommendations, segment customers based on customer data, diagnose disease based on previous symptoms and perform many other tasks.
During supervised learning, a machine is given data, known as training data in data mining parlance, based on which the machine does classification. For example, if a system is required to classify fruit, it would be given training data such as color, shapes, dimension and size. Based on this data, it would be able to classify fruit.
Usually a system requires multiple iterations of such process to be able to perform accurate classification. Since real-life classifications such as credit card fraud detection and disease classification are complex tasks, the machines need appropriate data and several iterations of learning sessions to achieve reasonable abilities.