Comparison of Machine Learning Algorithms Identifying Children at Increased Risk of Out‐of‐Home Placement: Development and Practical Considerations

The more complex XGBoost outperformed the simpler LASSO in predicting out-of-home placement and had less evidence of racial bias. This study highlights the complexities of developing predictive models in systems with known racial disparities and illustrates what can be accomplished when ML developers and policy leaders collaborate to maximize data to meet the needs of children and families.