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

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 th...

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Veröffentlicht in:	PloS one 2017-04, Vol.12 (4), p.e0174698-e0174698
Hauptverfasser:	Katz, Daniel Martin, Bommarito, 2nd, Michael J, Blackman, Josh
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Appeals Artificial intelligence Bayesian analysis Behavior Biology and Life Sciences Casts Classification Colleges & universities Complexity Computer and Information Sciences Computer simulation Construction Court decisions Decision trees Dental materials Ecology and Environmental Sciences Engineering Engineering and Technology Environmental law Federal court decisions Forecasting Forest management Fraud Historical account Human rights Humans Informatics Information processing Information retrieval Intellectual property Learning algorithms Leaves Lenses Machine Learning Markov chains Mathematical models Natural language (computers) Natural language processing Physical Sciences Physics Political science Politics Powers and duties Prediction markets Predictions Protocol (computers) R&D Research & development Research and Analysis Methods Science Social Justice - trends Social Sciences Speech Statistical mechanics Statistics Supreme Court Decisions Supreme court justices Taxation Technology Television United States
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