Abstract
Machine learning is currently one of the hottest topics that enable machines to learn from data and build predictions without being explicitly programmed for that task, automatically without human involvement. Supervised learning is one of two broad branches of machine learning that makes the model enable to predict future outcomes after they are trained based on past data where we use input/output pairs or the labeled data to train the model with the goal to produce a function that is approximated enough to be able to predict outputs for new inputs when introduced to them. Supervised learning problems can be grouped into regression problems and classification problems. A regression problem is when outputs are continuous whereas a classification problem is when outputs are categorical. This paper tries to compare different types of classification algorithms precisely widely used ones on the basis of some basic conceptions though it is obvious that a complete and comprehensive review and survey of all the supervised learning classification algorithms possibly cannot be accomplished by a single paper, but the references cited in this paper hopefully cover the significant theoretical issues and our survey has been kept limited to the widely used algorithms because the field is highly growing and not possible to cover all the algorithms in a single paper. One more point to be mentioned here that any study of complex procedure like neural networks has not been included as it has been tried to keep the content as much simple as possible.
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Sen, P.C., Hajra, M., Ghosh, M. (2020). Supervised Classification Algorithms in Machine Learning: A Survey and Review. In: Mandal, J., Bhattacharya, D. (eds) Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-7403-6_11
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