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Explainable Machine Learning for Predicting Homicide Clearance in the United States. (arXiv:2203.04768v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
Purpose: To explore the potential of Explainable Machine Learning in the
prediction and detection of drivers of cleared homicides at the national- and
state-levels in the United States.
Methods: First, nine algorithmic approaches are compared to assess the best
performance in predicting cleared homicides country-wise, using data from the
Murder Accountability Project. The most accurate algorithm among all (XGBoost)
is then used for predicting clearance outcomes state-wise. Second, SHAP, a
framework for Explainable Artificial Intelligence, is employed to capture the …
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