Principled Machine Learning Using the Super Learner: An Application to Predicting Prison Violence
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DOI: 10.1177/0049124117747301
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References listed on IDEAS
- Morris, Robert G. & Carriaga, Michael L. & Diamond, Brie & Piquero, Nicole Leeper & Piquero, Alex R., 2012. "Does prison strain lead to prison misbehavior? An application of general strain theory to inmate misconduct," Journal of Criminal Justice, Elsevier, vol. 40(3), pages 194-201.
- Franklin, Travis W. & Franklin, Cortney A. & Pratt, Travis C., 2006. "Examining the empirical relationship between prison crowding and inmate misconduct: A meta-analysis of conflicting research results," Journal of Criminal Justice, Elsevier, vol. 34(4), pages 401-412.
- Steiner, Benjamin & Butler, H. Daniel & Ellison, Jared M., 2014. "Causes and correlates of prison inmate misconduct: A systematic review of the evidence," Journal of Criminal Justice, Elsevier, vol. 42(6), pages 462-470.
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Cited by:
- Verhagen, Mark D., 2021. "Identifying and Improving Functional Form Complexity: A Machine Learning Framework," SocArXiv bka76, Center for Open Science.
- Ma, Ji, 2020. "Automated coding using machine-learning and remapping the U.S. nonprofit sector: A guide and benchmark," OSF Preprints pt3q9, Center for Open Science.
- Chao Wu & Pei Zheng & Xinyuan Xu & Shuhan Chen & Nasi Wang & Simon Hu, 2020. "Discovery of the Environmental Factors Affecting Urban Dwellers’ Mental Health: A Data-Driven Approach," IJERPH, MDPI, vol. 17(21), pages 1-16, November.
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Keywords
machine learning; ensemble methods; super learner; prisons; violence;All these keywords.
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