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Discriminant Analysis and Other Linear Classification Models

In: Applied Predictive Modeling

Author

Listed:
  • Max Kuhn

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

  • Kjell Johnson

    (Arbor Analytics)

Abstract

In this chapter we discuss models that classify samples using linear classification boundaries. We begin this chapter by describing a grant applications case study data set (Section 12.1) which will be used to illustrate models throughout this chapter as well as for Chapters 13-15. As foundational models, we discuss logistic regression (Section 12.2) and linear discriminant analysis (Section 12.3). In Section 12.4 we define and illustrates partial least squares discriminant analysis and its fundamental connection to linear discriminant analysis. Penalized models such as ridge penalty for logistic regression, glmnet, penalized linear discriminant analysis are discussed in Section 12.5. Nearest shrunken centroids, an approach tailored towards high dimensional data, is presented in Section 12.6. We demonstrate in the Computing Section (12.7) how to train each of these models in R. Finally, exercises are provided at the end of the chapter to solidify the concepts.

Suggested Citation

  • Max Kuhn & Kjell Johnson, 2013. "Discriminant Analysis and Other Linear Classification Models," Springer Books, in: Applied Predictive Modeling, chapter 0, pages 275-328, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4614-6849-3_12
    DOI: 10.1007/978-1-4614-6849-3_12
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