Supervised learning

Asimina Mertzani

Chapter 111 in Elgar Encyclopedia of Energy Economics, 2025, pp 429-430 from Edward Elgar Publishing

Abstract: Supervised learning is an educational activity that involves a learner and a teacher, in which the teacher guides the learner, aiming to increase the learner's knowledge. Although this is a type of learning often used by humans, nowadays it is mainly used to refer to supervised machine learning. This is a technique that uses advanced statistics and probabilistic theory to learn from labelled data to predict the right output given an unknown input. It is used to perform two main learning tasks: regression and classification. Some indicative examples of algorithms are Linear Models, K-Nearest Neighbors, Support Vector Machines, Naïve Bayes, Random Forest and Neural Networks. These algorithms have already been applied to multiple different fields, including the energy sector, to solve various problems. Overall, their effectiveness in learning from data patterns to provide the final answer makes supervised learning techniques promising tools for transforming future societies.

Keywords: Labelled Data; Machine Learning; Artificial Intelligence; Regression; Classification; Function Approximation (search for similar items in EconPapers)
Date: 2025
ISBN: 9781035310364
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