The random forest algorithm for statistical learning

Author:

Schonlau Matthias1,Zou Rosie Yuyan1

Affiliation:

1. University of Waterloo, Waterloo, Canada,

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.

Publisher

SAGE Publications

Subject

Mathematics (miscellaneous)

Reference12 articles.

1. Machine-learning Techniques in Economics

2. Dheeru D., Karra Taniskidou E. 2017. Default of credit card clients dataset. https://www.kaggle.com/uciml/default-of-credit-card-clients-dataset.

3. A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News

4. The WEKA data mining software

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