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

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  • Muchlinski, David
  • Siroky, David
  • He, Jingrui
  • Kocher, Matthew

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 L 1-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.

Suggested Citation

  • Muchlinski, David & Siroky, David & He, Jingrui & Kocher, Matthew, 2016. "Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data," Political Analysis, Cambridge University Press, vol. 24(1), pages 87-103, January.
    • Handle: RePEc:cup:polals:v:24:y:2016:i:1:p:87-103_9
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    3. David Siroky & Carolyn M. Warner & Gabrielle Filip-Crawford & Anna Berlin & Steven L. Neuberg, 2020. "Grievances and rebellion: Comparing relative deprivation and horizontal inequality," Conflict Management and Peace Science, Peace Science Society (International), vol. 37(6), pages 694-715, November.
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    9. Hofman, Jake M. & Goldstein, Daniel G. & Sen, Siddhartha & Poursabzi-Sangdeh, Forough & Allen, Jennifer & Dong, Ling Liang & Fried, Brenda & Gaur, Harpreet & Hoq, Adnan & Mbazor, Emeka & Moreira, Naom, 2021. "Expanding the scope of reproducibility research through data analysis replications," Organizational Behavior and Human Decision Processes, Elsevier, vol. 164(C), pages 192-202.
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    12. Phil Henrickson, 2020. "Predicting the costs of war," The Journal of Defense Modeling and Simulation, , vol. 17(3), pages 285-308, July.
    13. Vestby, Jonas & Buhaug, Halvard & von Uexkull, Nina, 2021. "Why do some poor countries see armed conflict while others do not? A dual sector approach," World Development, Elsevier, vol. 138(C).
    14. Marie K. Schellens & Salim Belyazid, 2020. "Revisiting the Contested Role of Natural Resources in Violent Conflict Risk through Machine Learning," Sustainability, MDPI, vol. 12(16), pages 1-29, August.
    15. Güneş Murat Tezcür & Clayton Besaw, 2020. "Jihadist waves: Syria, the Islamic State, and the changing nature of foreign fighters," Conflict Management and Peace Science, Peace Science Society (International), vol. 37(2), pages 215-231, March.
    16. Felix Ettensperger, 2020. "Comparing supervised learning algorithms and artificial neural networks for conflict prediction: performance and applicability of deep learning in the field," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(2), pages 567-601, April.
    17. Freire, Danilo, 2021. "Democratizing Policy Analytics with AutoML," Working Papers 11015, George Mason University, Mercatus Center.

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