Process of Machine Learning Methods

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Author(s): Hulya Kocyigit (Karamanoglu Mehmetbey University, Turkey)
Copyright: 2023
Pages: 38
Source title: Advancement in Business Analytics Tools for Higher Financial Performance
Source Author(s)/Editor(s): Reza Gharoie Ahangar (Lewis University, USA)and Mark Napier (Lewis University, USA)
DOI: 10.4018/978-1-6684-8386-2.ch001

Keywords: Accounting and Finance / Business Science Reference / Data Analysis & Statistics / Library & Information Science

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Abstract

The field of machine learning (ML) has grown to be a prominent subject within developed businesses while aiming to implement data-driven techniques to better day-to-day business activities. The reader is introduced to the most popular learning models in this chapter. Although unsupervised learning, reinforcement learning, and semi-supervised learning are incredibly important, the authors won't go into further detail about them here. This section will go into great length about supervised learning environments. The authors propose the following as a summary of the contributions to this chapter: Emphasizing some of the earlier works of literature that tackled these problems and discussing their limitations. In order to do so, the authors propose to review the regression family (i.e., simple regression and multiple linear regression) and decision tree family (i.e., CART, ID3, C4.5, chi-squared automatic interaction detection (CHAID), bagging and boosting). Examining the function and promise of ML techniques to resolve dilemmas and present potential implementation strategies.

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