Improving Supreme Court Forecasting Using Boosted Decision Trees

Aaron Kaufman, Peter Kraft, and Maya Sen
Forthcoming at Political Analysis

Abstract: Though used frequently in machine learning, AdaBoosted decision trees (ADTs) are rarely used in political science, despite having many properties that are useful for social science inquiries. In this paper, we explain how to use ADTs for social science predictions. We illustrate their use by examining a well-known political prediction problem, predicting U.S. Supreme Court rulings. We nd that our AdaBoosted approach out-performs 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.

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