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Machine Learning (Supervised)

In: Essentials of Business Analytics

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  • Shailesh Kumar

    (Reliance Jio)

Abstract

Every time we search the Web, buy a product online, swipe a credit card, or even check our e-mail, we are using a sophisticated machine learning system, built on a massive cloud platform, driving billions of decisions every day. Machine learning has many paradigms. In this chapter, we explore the philosophical, theoretical, and practical aspects of one of the most common machine learning paradigms—supervised learning—that essentially learns a mapping from an observation (e.g., symptoms and test results of a patient) to a prediction (e.g., disease or medical condition), which in turn is used to make decisions (e.g., prescription). This chapter explores the process, science, and art of building supervised learning models.

Suggested Citation

  • Shailesh Kumar, 2019. "Machine Learning (Supervised)," International Series in Operations Research & Management Science, in: Bhimasankaram Pochiraju & Sridhar Seshadri (ed.), Essentials of Business Analytics, chapter 0, pages 507-568, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-68837-4_16
    DOI: 10.1007/978-3-319-68837-4_16
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    Cited by:

    1. Pedro M. Mirete-Ferrer & Alberto Garcia-Garcia & Juan Samuel Baixauli-Soler & Maria A. Prats, 2022. "A Review on Machine Learning for Asset Management," Risks, MDPI, vol. 10(4), pages 1-46, April.

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