The random forest algorithm for statistical learning

University of Waterloo

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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.

Citation impact

1,254
total citations
FWCI
154.60
Percentile
100%
References
14
Citations per year

Authors

2

Topics & keywords

Keywords
  • Random forest
  • Computer science
  • Key (lock)
  • Artificial intelligence
  • Credit card
  • Machine learning
  • Algorithm
  • Statistical learning
UN Sustainable Development Goals
  • Life in Land
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