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Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data

Published online by Cambridge University Press:  04 January 2017

David Muchlinski*
Affiliation:
School of Social and Political Science, University of Glasgow, Glasgow, UK
David Siroky
Affiliation:
Department of Political Science, Arizona State University, Tempe, AZ, e-mail: david.siroky@asu.edu
Jingrui He
Affiliation:
Department of Computer Science and Engineering, Arizona State University, Tempe, AZ, e-mail: jingrui.he@asu.edu
Matthew Kocher
Affiliation:
Department of Political Science, Yale University, New Haven, CT, e-mail: mathew.kocher@yale.edu
*
e-mail: david.muchlinski@glasgow.ac.uk (corresponding author)

Abstract

The most commonly used statistical models of civil war onset fail to correctly predict most occurrences of this rare event in out-of-sample data. Statistical methods for the analysis of binary data, such as logistic regression, even in their rare event and regularized forms, perform poorly at prediction. We compare the performance of Random Forests with three versions of logistic regression (classic logistic regression, Firth rare events logistic regression, and L1-regularized logistic regression), and find that the algorithmic approach provides significantly more accurate predictions of civil war onset in out-of-sample data than any of the logistic regression models. The article discusses these results and the ways in which algorithmic statistical methods like Random Forests can be useful to more accurately predict rare events in conflict data.

Type
Articles
Copyright
Copyright © The Author 2015. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Author's note: Replication data are available on the Political Analysis Dataverse at http://dx.doi.org/10.7910/DVN/KRKWK8.

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