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Which method predicts recidivism best?: a comparison of statistical, machine learning and data mining predictive models

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  • N. Tollenaar
  • P. G. M. van der Heijden

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  • N. Tollenaar & P. G. M. van der Heijden, 2013. "Which method predicts recidivism best?: a comparison of statistical, machine learning and data mining predictive models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(2), pages 565-584, February.
    • Handle: RePEc:bla:jorssa:v:176:y:2013:i:2:p:565-584
    DOI: j.1467-985X.2012.01056.x
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    File URL: http://hdl.handle.net/10.1111/j.1467-985X.2012.01056.x
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    Cited by:

    1. Julia R. Falconer & Eibe Frank & Devon L. L. Polaschek & Chaitanya Joshi, 2024. "Eliciting Informative Priors by Modeling Expert Decision Making," Decision Analysis, INFORMS, vol. 21(2), pages 77-90, June.
    2. Kigerl, Alex & Hamilton, Zachary & Kowalski, Melissa & Mei, Xiaohan, 2022. "The great methods bake-off: Comparing performance of machine learning algorithms," Journal of Criminal Justice, Elsevier, vol. 82(C).
    3. Raffaele Mattera & Philipp Otto, 2023. "Network log-ARCH models for forecasting stock market volatility," Papers 2303.11064, arXiv.org.
    4. Guido Vittorio Travaini & Federico Pacchioni & Silvia Bellumore & Marta Bosia & Francesco De Micco, 2022. "Machine Learning and Criminal Justice: A Systematic Review of Advanced Methodology for Recidivism Risk Prediction," IJERPH, MDPI, vol. 19(17), pages 1-13, August.
    5. Jiaming Zeng & Berk Ustun & Cynthia Rudin, 2017. "Interpretable classification models for recidivism prediction," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 689-722, June.
    6. Cynthia Rudin & Berk Ustun, 2018. "Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice," Interfaces, INFORMS, vol. 48(5), pages 449-466, October.

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