Predicting Arrest Release Outcomes: A Comparative Analysis of Machine Learning Models

This comparative study evaluates machine learning models for predicting arrest release outcomes using 5,226 marijuana possession cases from the Toronto Police Service (1997-2002). The dataset exhibited significant class imbalance, with only 17.1% detention outcomes versus 82.9% releases. After prepr...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:al-Tarbiyah wa-al-ʻilm lil-ʻulūm al-insānīyah : majallah ʻilmīyah muḥakkamah taṣduru ʻan Kullīyat al-Tarbiyah lil-ʻUlūm al-Insānīyah fī Jāmiʻat al-Mawṣil Ročník 34; číslo 4; s. 62 - 73
Hlavní autori: Adebayo, O. P., Ibrahim, Ahmed, Oyeleke, K.T.
Médium: Journal Article
Jazyk:Arabic
English
Vydavateľské údaje: College of Education for Pure Sciences 01.10.2025
Predmet:
bail decision prediction
bias and fairness in ml
classification algorithms
comparative analysis
predicting
ISSN:1812-125X, 2664-2530
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:This comparative study evaluates machine learning models for predicting arrest release outcomes using 5,226 marijuana possession cases from the Toronto Police Service (1997-2002). The dataset exhibited significant class imbalance, with only 17.1% detention outcomes versus 82.9% releases. After preprocessing to handle missing values and convert categorical variables, we implemented two modeling approaches: a 500-tree Random Forest classifier with feature importance measurement and a binomial Logistic Regression model. Both algorithms demonstrated strong predictive capability for release cases, achieving comparable overall accuracy (83.2-83.4%) and excellent sensitivity (>98%), though they struggled with the critical minority class as evidenced by poor specificity (<7%). The models showed similar discriminative power, with Logistic Regression achieving a marginally higher AUC-ROC (0.733 vs 0.726). Feature importance analysis identified employment status and prior police background checks as the strongest predictors, while demographic factors, including race, also contributed significantly to predictions. These results highlight both the technical challenges of imbalanced classification in justice system data and the ethical considerations surrounding potential algorithmic bias, particularly given the high false positive rate for detention predictions that could exacerbate existing disparities. The study underscores the need for careful model evaluation and responsible implementation when applying predictive analytics to sensitive criminal justice decisions, balancing statistical performance with considerations of fairness and social impact.
ISSN:1812-125X
2664-2530
DOI:10.33899/jes.v34i4.49670