Comparative Analysis of Machine Learning Models in Predicting Recidivism
Research Article
Open Access
CC BY

Comparative Analysis of Machine Learning Models in Predicting Recidivism

Ruiyang Wang 1* Zihan Gu 2, Qihao Su 3
1 University College London
2 Nanjing University of Finance and Economics
3 Sichuan University
*Corresponding author: zcahrw4@ucl.ac.uk
Published on 9 September 2025
Journal Cover
AEMPS Vol.213
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-351-2
ISBN (Online): 978-1-80590-352-9
Download Cover

Abstract

Predicting whether a criminal is likely to commit another crime is an important research topic in the fields of criminology and sociology. This article explores various factors that affect the likelihood of recidivism among criminals, including age, gender, and social race. Through a comprehensive literature review and statistical analysis of the impact of different factors on the number of criminals, this study uses data science to investigate which factors affect the recidivism rate of crime, and proposes some key factors for predicting the likelihood of criminal recidivism. Finally, this article explores how to use these predictive factors to improve the prediction and intervention measures of crime recidivism rates, and establishes relevant models to predict the risk of crime recidivism, such as Random Forest model logistic regression model, decision tree model, and support vector machine model. The accuracy of these four models is compared and analyzed.

Keywords:

recidivism, decision tree, logistic regression, support vector machine, random forest.

View PDF
Wang,R.;Gu,Z.;Su,Q. (2025). Comparative Analysis of Machine Learning Models in Predicting Recidivism. Advances in Economics, Management and Political Sciences,213,65-75.

References

[1]. Chouldechova, A. (2017).Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data., 5(2): 153–163.

[2]. Dressel, J., & Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. Science Advances., 4(1): eaao5580.

[3]. Gendreau, P., Little, T., & Goggin, C. (1996) A meta-analysis of the predictors of adult offender recidivism: What works! Criminology., 34(4): 575–608.

[4]. Hayes, B. (2018) Predicting criminal recidivism with R. https: //benhay.es/posts/predicting-criminal-recidivism-r/#top

[5]. Jeff, L., & Surya, M., & Lauren, K. (2016) How do we analyze the COMPAS recidivism algorithm. https: //www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm

[6]. Hosmer, D. W., & Lemeshow, S. (2000) Applied logistic regression (2nd ed.). Wiley. ISBN 978-0-471-35632-5.

[7]. Boyd, S., & Vandenberghe, L. (2004) Convex optimization. Cambridge University Press. ISBN 978-0-521-83378-3.

[8]. Cortes, C., & Vapnik, V. (1995) Support-vector networks. Machine Learning., 20(3): 273–297.

[9]. Awad, M., & Khanna, R. (2015) Support vector machines for classification. In Efficient learning machines. pp. 39–66.

[10]. Krishnan, S. (2021) Decision tree for classification: Entropy and information gain. Medium. https: //medium.com/codex/decision-tree-for-classification-entropy-and-information-gain-cd9f99a26e0d

[11]. Pennsylvania State University. (n.d.) 11.8.2 - Assessing the adequacy of the model | STAT 508. Online Learning, Penn State. https: //online.stat.psu.edu/stat508/lesson/11/11.8/11.8.2

References

[1]. Chouldechova, A. (2017).Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data., 5(2): 153–163.

[2]. Dressel, J., & Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. Science Advances., 4(1): eaao5580.

[3]. Gendreau, P., Little, T., & Goggin, C. (1996) A meta-analysis of the predictors of adult offender recidivism: What works! Criminology., 34(4): 575–608.

[4]. Hayes, B. (2018) Predicting criminal recidivism with R. https: //benhay.es/posts/predicting-criminal-recidivism-r/#top

[5]. Jeff, L., & Surya, M., & Lauren, K. (2016) How do we analyze the COMPAS recidivism algorithm. https: //www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm

[6]. Hosmer, D. W., & Lemeshow, S. (2000) Applied logistic regression (2nd ed.). Wiley. ISBN 978-0-471-35632-5.

[7]. Boyd, S., & Vandenberghe, L. (2004) Convex optimization. Cambridge University Press. ISBN 978-0-521-83378-3.

[8]. Cortes, C., & Vapnik, V. (1995) Support-vector networks. Machine Learning., 20(3): 273–297.

[9]. Awad, M., & Khanna, R. (2015) Support vector machines for classification. In Efficient learning machines. pp. 39–66.

[10]. Krishnan, S. (2021) Decision tree for classification: Entropy and information gain. Medium. https: //medium.com/codex/decision-tree-for-classification-entropy-and-information-gain-cd9f99a26e0d

[11]. Pennsylvania State University. (n.d.) 11.8.2 - Assessing the adequacy of the model | STAT 508. Online Learning, Penn State. https: //online.stat.psu.edu/stat508/lesson/11/11.8/11.8.2

Cite this article

Wang,R.;Gu,Z.;Su,Q. (2025). Comparative Analysis of Machine Learning Models in Predicting Recidivism. Advances in Economics, Management and Political Sciences,213,65-75.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of the 4th International Conference on Financial Technology and Business Analysis

ISBN: 978-1-80590-351-2(Print) / 978-1-80590-352-9(Online)
Editor: Lukáš Vartiak
Conference website: https://2025.icftba.org/
Conference date: 12 December 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.213
ISSN: 2754-1169(Print) / 2754-1177(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

For authors
For editors
For reviewers

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.