book chapter · Apress eBooks · 2025

Random Forest

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Abstract

In this chapter, we will look at random forests – a versatile, non-linear model that can be used for both regression and classification. Random forests have an impressive mathematical and statistical pedigree to be labeled as advanced statistical models, and their use of decision trees as weak learners and their reliance on bagging of data for training an ensemble of decision trees makes them among the more widely adopted machine learning tools.

Cite this paper

APA 7

Ahlawat, S. (2025). Random Forest. Apress eBooks, 219–239. https://doi.org/10.1007/979-8-8688-0962-0_8

MLA 9

Ahlawat, Sonika. “Random Forest.” Apress eBooks, 2025, pp. 219–39, https://doi.org/10.1007/979-8-8688-0962-0_8.

Chicago

Ahlawat, Sonika. 2025. “Random Forest.” Apress eBooks: 219–39. https://doi.org/10.1007/979-8-8688-0962-0_8.

Harvard

Ahlawat, S. (2025) ‘Random Forest’, Apress eBooks, pp. 219–239. Available at: https://doi.org/10.1007/979-8-8688-0962-0_8.

BibTeX

@incollection{ahlawat2025random,
  title = {{Random Forest}},
  author = {Ahlawat, Sonika},
  booktitle = {Apress eBooks},
  pages = {219--239},
  year = {2025},
  doi = {10.1007/979-8-8688-0962-0_8}
}

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