Improving Supreme Court Forecasting Using Boosted Decision Trees. (19th February 2019)

• Record Type: Journal Article
• Authors: Kaufman, Aaron Russell; Kraft, Peter; Sen, Maya
• Is Part Of: Political analysis. Volume 27:Number 3(2019)
• Page Start: 381
• Page End: 387
• Publication Date: 2019-02-19
• Subjects: statistical analysis of texts, -- forecasting, -- Learning; Political science -- Methodology -- Periodicals; Electronic journals; 320.011
• DOI: 10.1017/pan.2018.59
• Languages: English
• ISSNs: 1047-1987

Abstract

Abstract : Though used frequently in machine learning, boosted decision trees are largely unused in political science, despite many useful properties. We explain how to use one variant of boosted decision trees, AdaBoosted decision trees (ADTs), for social science predictions. We illustrate their use by examining a well-known political prediction problem, predicting U.S. Supreme Court rulings. We find that our ADT approach outperforms existing predictive models. We also provide two additional examples of the approach, one predicting the onset of civil wars and the other predicting county-level vote shares in U.S. presidential elections.