IDEAS home Printed from https://ideas.repec.org/r/cup/polals/v24y2016i1p87-103_9.html

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

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


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. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. Christian Oswald, 2026. "I Still Haven’t Found what I’m Looking for: Predicting Security-Related Incidents and Conflict Fatalities with Google Trends and Wikipedia Data," Journal of Conflict Resolution, Peace Science Society (International), vol. 70(2-3), pages 499-524, March.
  13. Julia Semmelbeck & Clayton Besaw, 2020. "Exploring the Determinants of Crime-Terror Cooperation using Machine Learning," Journal of Quantitative Criminology, Springer, vol. 36(3), pages 527-558, September.
  14. 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.
  15. repec:osf:socarx:tvshu_v1 is not listed on IDEAS
  16. Phil Henrickson, 2020. "Predicting the costs of war," The Journal of Defense Modeling and Simulation, , vol. 17(3), pages 285-308, July.
  17. 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).
  18. 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.
  19. 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.
  20. 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.
  21. Dominic Rohner, 2025. "Conflict," CESifo Working Paper Series 12035, CESifo.
  22. 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.
  23. Marius Radean & Andreas Beger, 2025. "Not-so-average after all: Individual vs. aggregate effects in substantive research," Journal of Peace Research, Peace Research Institute Oslo, vol. 62(7), pages 2408-2423, December.
  24. 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).
  25. 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).
  26. 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.
  27. 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.
  28. Hu, Lijiao & Zheng, Yuqing, 2026. "Do food safety certifications improve the safety of our food system? evidence from the U.S. Meat, Poultry, and egg industry," Food Policy, Elsevier, vol. 138(C).
  29. 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.
  30. Freire, Danilo, 2021. "Democratizing Policy Analytics with AutoML," Working Papers 11015, George Mason University, Mercatus Center.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.