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Classification

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Abstract

In this chapter, we consider constructing a classification rule from covariates to a response that takes values from a finite set such as ± 1, figures 0, 1, ⋯ , 9. For example, we wish to classify a postal code from handwritten characters and to make a rule between them. First, we consider logistic regression to minimize the error rate in the test data after constructing a classifier based on the training data. The second approach is to draw borders that separate the regions of the responses with linear and quadratic discriminators and the k-nearest neighbor algorithm. The linear and quadratic discriminations draw linear and quadratic borders, respectively, and both introduce the notion of prior probability to minimize the average error probability. The k-nearest neighbor method searches the border more flexibly than the linear and quadratic discriminators. On the other hand, we take into account the balance of two risks, such as classifying a sick person as healthy and classifying a healthy person as unhealthy. In particular, we consider an alternative approach beyond minimizing the average error probability. The regression method in the previous chapter and the classification method in this chapter are two significant issues in the field of machine learning.

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  • DOI: 10.1007/978-981-15-7877-9_3
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Notes

  1. 1.

    In this chapter, instead of \(\beta \in {\mathbb R}^{p+1}\), we separate the slope \(\beta \in {\mathbb R}^p\) and the intercept \(\beta _0\in {\mathbb R}\).

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Suzuki, J. (2021). Classification. In: Statistical Learning with Math and Python. Springer, Singapore. https://doi.org/10.1007/978-981-15-7877-9_3

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  • DOI: https://doi.org/10.1007/978-981-15-7877-9_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-7876-2

  • Online ISBN: 978-981-15-7877-9

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