The random forest algorithm for statistical learning

Matthias Schonlau () and Rosie Yuyan Zou ()
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Matthias Schonlau: University of Waterloo
Rosie Yuyan Zou: University of Waterloo

Stata Journal, 2020, vol. 20, issue 1, 3-29

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.

Keywords: rforest; random decision forest algorithm (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1177/1536867X20909688

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