Improving Supreme Court Forecasting Using Boosted Decision Trees

Aaron Russell Kaufman, Peter Kraft, Maya Sen

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)381-387
Number of pages7
JournalPolitical Analysis
Issue number3
StatePublished - Jul 19 2019


  • Learning
  • forecasting
  • statistical analysis of texts

ASJC Scopus subject areas

  • Sociology and Political Science
  • Political Science and International Relations


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