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An Introduction to Feature Selection

In: Applied Predictive Modeling

Author

Listed:
  • Max Kuhn

    (Pfizer Global Research and Development, Division of Nonclinical Statistics)

  • Kjell Johnson

    (Arbor Analytics)

Abstract

Determining which predictors should be included in a model is becoming one of the most critical questions as data are becoming increasingly high-dimensional. The chapter demonstrates the negative effect of extra predictors on a number of models (Section 19.1), as well as discussing typical approaches to supervised feature selection such as wrapper and filter methods (Sections 19.2-19.4). The modeler should also be aware of the danger of selection bias and how to avoid it (Section 19.5). In Section 19.6 we present a case study to illustrate the feature selection methods. In the Computing Section (19.7) we demonstrate how to implement feature selection methodologies in R. Finally, exercises are provided at the end of the chapter to solidify the concepts.

Suggested Citation

  • Max Kuhn & Kjell Johnson, 2013. "An Introduction to Feature Selection," Springer Books, in: Applied Predictive Modeling, chapter 0, pages 487-519, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4614-6849-3_19
    DOI: 10.1007/978-1-4614-6849-3_19
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    Cited by:

    1. Bhaswatee Baishya & Arup Kumar Sarma, 2026. "An Easy-to-Apply Machine Learning Framework for Hydrologic Evaluation of Ungauged Catchments," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 40(1), pages 1-22, January.

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