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

By David Muchlinski, David S. Siroky, Jingrui He, M. Kocher. 2016

244 citations recorded by the source corpus.

Subfields: Political Science and International Relations, Sociology and Political Science

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In Sociology and Political Science

1. Rafael Valle
2. Jason Poulos

Citations in the corpus

• Missing Data Imputation for Supervised Learning (2016, background): “Supervised learning has become an increasingly attractive methodology and proven to be effective in social science applications, such as studies of international and civil conflict (Beck, King, and Zeng 2000; Hill and Jones 2014; Muchlinski et al. 2016) and election fraud (Cant and Saiegh 2011; Mont”