Abstract
Random forests (Breiman, 2001, Machine Learning 45: 5–32) is a statistical- or machine-learning algorithm for prediction. In this article, we introduce a corresponding new command, rforest. We overview the random forest algorithm and illustrate its use with two examples: The first example is a classification 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 logscaled number of shares of online news articles. We conclude with a discussion that summarizes key points demonstrated in the examples.
Keywords
Random forestComputer scienceKey (lock)Artificial intelligenceCredit cardMachine learningAlgorithmStatistical learningWorld Wide Web
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Publication Info
- Year
- 2020
- Type
- article
- Volume
- 20
- Issue
- 1
- Pages
- 3-29
- Citations
- 1126
- Access
- Closed
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Cite This
Matthias Schonlau,
Rosie Yuyan Zou
(2020).
The random forest algorithm for statistical learning.
The Stata Journal Promoting communications on statistics and Stata
, 20
(1)
, 3-29.
https://doi.org/10.1177/1536867x20909688
Identifiers
- DOI
- 10.1177/1536867x20909688