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Statistics, Data Mining, and Machine Learning in AstronomyA Practical Python Guide for the Analysis of Survey Data$
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Željko Ivezic, Andrew J. Connolly, Jacob T VanderPlas, and Alexander Gray

Print publication date: 2014

Print ISBN-13: 9780691151687

Published to Princeton Scholarship Online: October 2017

DOI: 10.23943/princeton/9780691151687.001.0001

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Classification

Classification

Chapter:
(p.365) 9 Classification
Source:
Statistics, Data Mining, and Machine Learning in Astronomy
Author(s):

Andrew J. Connolly

Jacob T. VanderPlas

Alexander Gray

Andrew J. Connolly

Jacob T. VanderPlas

Alexander Gray

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691151687.003.0009

Chapter 6 described techniques for estimating joint probability distributions from multivariate data sets and for identifying the inherent clustering within the properties of sources. This approach can be viewed as the unsupervised classification of data. If, however, we have labels for some of these data points (e.g., an object is tall, short, red, or blue) we can utilize this information to develop a relationship between the label and the properties of a source. We refer to this as supervised classification, which is the focus of this chapter. The motivation for supervised classification comes from the long history of classification in astronomy. Possibly the most well known of these classification schemes is that defined by Edwin Hubble for the morphological classification of galaxies based on their visual appearance. This chapter discusses generative classification, k-nearest-neighbor classifier, discriminative classification, support vector machines, decision trees, and evaluating classifiers.

Keywords:   supervised classification, generative classification, k-nearest-neighbor classifier, discriminative classification, support vector machines, decision trees, classifiers

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