other · 2015

Ensemble Methods

Cited by 0Abstract and details only

Abstract

Ensemble methods stem from the observation that multiple models give better performance than a single model if the models are somewhat independent of one another. The key with ensemble methods is to develop an algorithm approach to generate numerous somewhat independent models that will then be combined into an ensemble. This chapter shows the reader how the most popular methods accomplish this. It teaches the mechanics of the most popular ensemble methods, and outlines the basic structure of the algorithms and demonstrates the algorithms in Python code to give a firm understanding of their workings. The chapter primarily uses binary decision trees as base learners. Some widely used upper-level algorithms are known as bagging, gradient boosting, and random forests are coded in this chapter to provide a better understand the options, input variables, nominal starting values, and so on for the Python packages for these algorithms.

Cite this paper

APA 7

(2015). Ensemble Methods. https://doi.org/10.1002/9781119183600.ch6

MLA 9

“Ensemble Methods.” 2015, pp. 211–53, https://doi.org/10.1002/9781119183600.ch6.

Chicago

2015. “Ensemble Methods.” https://doi.org/10.1002/9781119183600.ch6.

Harvard

(2015) ‘Ensemble Methods’. Available at: https://doi.org/10.1002/9781119183600.ch6.

BibTeX

@misc{anon2015ensemble,
  title = {{Ensemble Methods}},
  pages = {211--253},
  year = {2015},
  doi = {10.1002/9781119183600.ch6}
}

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