Tag: random forest

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Predicting United States Policy Outcomes with Random Forests (via arXiv)

Daniel Katz

Interesting paper which follows on to a number of Machine Learning / NLP driven Legislative Prediction or Government Prediction papers. Access the draft of paper from arXiv. 

For more examples, see e.g. the follow papers —

Gerrish SM, Blei DM. “Predicting legislative roll calls from text”. ICML, 2011.

Yano T, Smith NA, Wilkerson JD. “Textual Predictors of Bill Survival in Congressional Committees”. Proc 2012 Conf N Amer Chapter Assoc Comp Linguistics, Human Language Technologies, 2012.

Katz DM, Bommarito MJ, Blackman J. “A general approach for predicting the
behavior of the Supreme Court of the United States”. PLOS One, 2017.

Nay, J. “Predicting and Understanding Law Making with Word Vectors and an Ensemble Model.” PLOS One, 2017.

Waltl, Bernhard Ernst. “Semantic Analysis and Computational Modeling of Legal Documents.” PhD diss., Technische Universität München, 2018.

Davoodi, Maryam, Eric Waltenburg, and Dan Goldwasser. “Understanding the Language of Political Agreement and Disagreement in Legislative Texts.” In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5358-5368. 2020.

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A General Approach for Predicting the Behavior of the Supreme Court of the United States (Paper Version 2.01) (Katz, Bommarito & Blackman)

clsadmin

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Long time coming for us but here is Version 2.01 of our #SCOTUS Paper …

We have added three times the number years to the prediction model and now predict out-of-sample nearly two centuries of historical decisions (1816-2015). Then, we compare our results to three separate null models (including one which leverages in-sample information).

Here is the abstract:  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. Our model leverages the random forest method together with unique feature engineering to predict nearly two centuries of historical decisions (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an advance for the science of quantitative legal prediction and portend a range of other potential applications.