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

Citations

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Cited by:

  1. Songul Cinaroglu, 2020. "Modelling unbalanced catastrophic health expenditure data by using machine‐learning methods," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(4), pages 168-181, October.
  2. Cihan Şahin, 2023. "Predicting base station return on investment in the telecommunications industry: Machine‐learning approaches," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 30(1), pages 29-40, January.
  3. Benedikt Langenberger & Timo Schulte & Oliver Groene, 2023. "The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data," PLOS ONE, Public Library of Science, vol. 18(1), pages 1-16, January.
  4. Liam F. Beiser-McGrath & Robert A. Huber, 2018. "Assessing the relative importance of psychological and demographic factors for predicting climate and environmental attitudes," Climatic Change, Springer, vol. 149(3), pages 335-347, August.
  5. 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.
  6. John Cuffe & Sudip Bhattacharjee & Ugochukwu Etudo & Justin C. Smith & Nevada Basdeo & Nathaniel Burbank & Shawn R. Roberts, 2019. "Using Public Data to Generate Industrial Classification Codes," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 229-246, National Bureau of Economic Research, Inc.
  7. Dominic Rohner, 2025. "Conflict," CESifo Working Paper Series 12035, CESifo.
  8. 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.
  9. 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.
  10. Ku, Arthur Lin & Qiu, Yueming (Lucy) & Lou, Jiehong & Nock, Destenie & Xing, Bo, 2022. "Changes in hourly electricity consumption under COVID mandates: A glance to future hourly residential power consumption pattern with remote work in Arizona," Applied Energy, Elsevier, vol. 310(C).
  11. Abdel Latef Anouze & Imad Bou-Hamad, 2021. "Inefficiency source tracking: evidence from data envelopment analysis and random forests," Annals of Operations Research, Springer, vol. 306(1), pages 273-293, November.
  12. Mark Musumba & Naureen Fatema & Shahriar Kibriya, 2021. "Prevention Is Better Than Cure: Machine Learning Approach to Conflict Prediction in Sub-Saharan Africa," Sustainability, MDPI, vol. 13(13), pages 1-18, July.
  13. Zhaochen He & John Camobreco & Keith Perkins, 2022. "How he won: Using machine learning to understand Trump’s 2016 victory," Journal of Computational Social Science, Springer, vol. 5(1), pages 905-947, May.
  14. Alfred Krzywicki & David Muchlinski & Benjamin E. Goldsmith & Arcot Sowmya, 2022. "From academia to policy makers: a methodology for real-time forecasting of infrequent events," Journal of Computational Social Science, Springer, vol. 5(2), pages 1489-1510, November.
  15. Macis, Luca & Tagliapietra, Marco & Meo, Rosa & Pisano, Paola, 2024. "Breaking the trend: Anomaly detection models for early warning of socio-political unrest," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
  16. Stefano Benati & Matteo Bon & Filippo Nardi, 2025. "Exploring the predictors of the populist vote using random forests," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(2), pages 1393-1426, April.
  17. 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.
  18. Rost, Nicolas & Ronco, Michele, 2026. "Anticipating humanitarian emergencies with a high risk of conflict-induced displacement," International Journal of Forecasting, Elsevier, vol. 42(1), pages 138-157.
  19. Antonietta di Salvatore & Mirko Moscatelli, 2024. "Improving survey information on household debt using granular credit databases," Questioni di Economia e Finanza (Occasional Papers) 839, Bank of Italy, Economic Research and International Relations Area.
  20. 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.
  21. Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021. "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, vol. 37(1), pages 360-377.
  22. Mueller, Hannes & Rauh, Christopher, 2018. "Reading Between the Lines: Prediction of Political Violence Using Newspaper Text," American Political Science Review, Cambridge University Press, vol. 112(2), pages 358-375, May.
  23. repec:osf:socarx:tvshu_v1 is not listed on IDEAS
  24. Freire, Danilo, 2021. "Democratizing Policy Analytics with AutoML," Working Papers 11015, George Mason University, Mercatus Center.
  25. Phil Henrickson, 2020. "Predicting the costs of war," The Journal of Defense Modeling and Simulation, , vol. 17(3), pages 285-308, July.
  26. 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).
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