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Improving Supreme Court Forecasting Using Boosted Decision Trees

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  • Kaufman, Aaron Russell
  • Kraft, Peter
  • Sen, Maya

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.

Suggested Citation

  • Kaufman, Aaron Russell & Kraft, Peter & Sen, Maya, 2019. "Improving Supreme Court Forecasting Using Boosted Decision Trees," Political Analysis, Cambridge University Press, vol. 27(3), pages 381-387, July.
    • Handle: RePEc:cup:polals:v:27:y:2019:i:03:p:381-387_00
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    Cited by:

    1. Chee Sun Lee & Peck Yeng Sharon Cheang & Massoud Moslehpour, 2022. "Predictive Analytics in Business Analytics: Decision Tree," Advances in Decision Sciences, Asia University, Taiwan, vol. 26(1), pages 1-30, March.
    2. Netta Barak‐Corren & Yoav Kan‐Tor & Nelson Tebbe, 2022. "Examining the effects of antidiscrimination laws on children in the foster care and adoption systems," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 19(4), pages 1003-1066, December.

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