Machine Learning and Risk Assessment: Random Forest Does Not Outperform Logistic Regression in the Prediction of Sexual Recidivism.
Although many studies supported the use of actuarial risk assessment instruments (ARAIs) because they outperformed unstructured judgments, it remains an ongoing challenge to seek potentials for improvement of their predictive performance. Machine learning (ML) algorithms, like random forests, are ab...
| Publicado en: | Assessment Vol. 31; no. 2; pp. 460 - 482 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Mar2024
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| Acceso en línea: | Ver este registro en EBSCOhost |