A general approach for predicting the behavior of the Supreme Court of the United States

@article{Katz2016AGA,
  title={A general approach for predicting the behavior of the Supreme Court of the United States},
  author={Daniel Martin Katz and Michael James Bommarito and Josh Blackman},
  journal={PLoS ONE},
  year={2016},
  volume={12}
}
Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time-evolving random forest classifier that leverages unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our… 

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