Predicting criminal recidivism using specialized feature engineering and XGBoost
Published in CrimRxiv, 2021
Citation: Rajendran, S., & Sundararajan, P. (2021). Predicting criminal recidivism using specialized feature engineering and XGBoost. CrimRxiv. https://doi.org/10.21428/cb6ab371.d95f8c48
As the research, development, and evaluation agency of the U.S. Department of Justice, NIJ invests in scientific research across diverse disciplines to serve the needs of the criminal justice community. In 2021, NIJ released the “Recidivism Forecasting Challenge.” With this Challenge, NIJ aims to: 1 - encourage “non-criminal justice” forecasting researchers to compete against more “traditional” criminal justice forecasting researchers, building upon the current knowledge base while infusing innovative, new perspectives; and 2 - compare available forecasting methods in an effort to improve person-based and place-based recidivism forecasting3. Our team entered into the Small Team category of the challenge and aimed to utilize state of the art machine learning techniques to assist in this field.