Discriminant Analysis and Other Linear Classification Models

Max Kuhn and Kjell Johnson
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Max Kuhn: Pfizer Global Research and Development, Division of Nonclinical Statistics
Kjell Johnson: Arbor Analytics

Chapter Chapter 12 in Applied Predictive Modeling, 2013, pp 275-328 from Springer

Abstract: 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.

Keywords: Partial Little Square; Linear Discriminant Analysis; Class Probability; Partial Little Square Discriminant Analysis; Grant Application (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4614-6849-3_12

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DOI: 10.1007/978-1-4614-6849-3_12

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