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The random forest algorithm for statistical learning

Author

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  • Matthias Schonlau

    (University of Waterloo)

  • Rosie Yuyan Zou

    (University of Waterloo)

Abstract

Random forests (Breiman, 2001, Machine Learning 45: 5–32) is a statistical- or machine-learning algorithm for prediction. In this article, we intro- duce a corresponding new command, rforest. We overview the random forest algorithm and illustrate its use with two examples: The first example is a clas- sification problem that predicts whether a credit card holder will default on his or her debt. The second example is a regression problem that predicts the log- scaled number of shares of online news articles. We conclude with a discussion that summarizes key points demonstrated in the examples.

Suggested Citation

  • Matthias Schonlau & Rosie Yuyan Zou, 2020. "The random forest algorithm for statistical learning," Stata Journal, StataCorp LP, vol. 20(1), pages 3-29, March.
    • Handle: RePEc:tsj:stataj:v:20:y:2020:i:1:p:3-29
    DOI: 10.1177/1536867X20909688
    Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-1/st0587/
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    Keywords

    rforest; random decision forest algorithm;

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