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Predicting and understanding law-making with word vectors and an ensemble model
Out of nearly 70,000 bills introduced in the U.S. Congress from 2001 to 2015, only 2,513 were enacted. We developed a machine learning approach to forecasting the probability that any bill will become law. Starting in 2001 with the 107th Congress, we trained models on data from previous Congresses,...
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Public Library of Science
2017
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5425031/ https://www.ncbi.nlm.nih.gov/pubmed/28489868 http://dx.doi.org/10.1371/journal.pone.0176999 |