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Cited by 234Political Conflict and GovernanceImbalanced Data Classification TechniquesComputational and Text Analysis Methods

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

Muchlinski, David, Siroky, David, He, Jingrui, Kocher, Matthew·2016·Political Analysis
DOI 10.1093/pan/mpv024
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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, andL1-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.

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Crossref · 10.1093/pan/mpv024

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61 of 61 references verified0 citation errors · 0 not checkable

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  • Over the last few years, the field of civil war prediction has switched to predictive modeling using complex ML models such as Random Forests and Adaboost instead of LR.

    “Over the last few years, this field has switched to predictive modeling using complex ML models such as Random Forests and Adaboost instead of LR (see Figure 2), with several papers claiming near-perfect performance of these models for civil war prediction.75,76,77,78”

    From Leakage and the reproducibility crisis in machine-learning-based science (2023) · cites this paper as [75]

  • Muchlinski et al. impute the training and test data together.

    “Muchlinski et al.75 impute the training and test data together”

    From Leakage and the reproducibility crisis in machine-learning-based science (2023) · cites this paper as [75]

  • Several papers claim near-perfect performance of complex ML models for civil war prediction.

    “Over the last few years, this field has switched to predictive modeling using complex ML models such as Random Forests and Adaboost instead of LR (see Figure 2), with several papers claiming near-perfect performance of these models for civil war prediction.75,76,77,78”

    From Leakage and the reproducibility crisis in machine-learning-based science (2023) · cites this paper as [75]

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Related

Authors
Muchlinski, D., Siroky, D., He, J., & Kocher, M.
Affiliations
  • University of Glasgow (GB)
  • Arizona State University (US)
  • Yale University (US)
Corresponding author
Muchlinski, David
ORCID
Venue
Political Analysis
Publisher
Cambridge University Press (CUP)
Published
2016
Volume
24
Issue
1
Pages
87-103
ISSN
  • 1047-1987 (ISSN-L)
  • 1476-4989
DOI
10.1093/pan/mpv024
OpenAlex
W2278972578
Cited by
234
References
65
Citations by year
2026: 16 · 2025: 23 · 2024: 19 · 2023: 17 · 2022: 37 · 2021: 29 · 2020: 29 · 2019: 25 · 2018: 24 · 2017: 14 · 2016: 1
FWCI
43.12
Type
journal article
Language
en
Open access
closed
Licence
www.cambridge.org/core/terms (unspecified, 369 d)
Locations
  • Political Analysis (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • HighWire Press Open Archive (submittedVersion)
Topics
  • Social Sciences › Sociology and Political Science › Political Conflict and Governance
  • Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
Keywords
civil war onset, random forest, logistic regression, class imbalance, rare events, Firth logistic regression, L1-regularized logistic regression, out-of-sample forecasting, conflict prediction

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