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The random forest algorithm for statistical learning

Schonlau, Matthias ; Zou, Rosie Yuyan

The Stata journal, 2020-03, Vol.20 (1), p.3-29 [Periódico revisado por pares]

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Título:
The random forest algorithm for statistical learning
Autor: Schonlau, Matthias ; Zou, Rosie Yuyan
É parte de: The Stata journal, 2020-03, Vol.20 (1), p.3-29
Descrição: 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.
Editor: Los Angeles, CA: SAGE Publications
Idioma: Inglês

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