Since research is needed to identify optimal scenarios for algorithm use in assessment development, we compared regression models (logistic, boosted, and penalized) to more advanced, techniques (neural networks, support vector machines, random forests, and K-nearest neighbors); while also introducing ‘stacking’, a method that combines algorithms to create an optimized model.
Using a multi-state sample of 258,464 youth assessments, we varied prediction scenarios by sample size and base rate. While performance generally improved with greater sample size, a set of ‘top performing’ algorithms was identified. Among top performers, a ‘saturation point’ was observed, where algorithm type had little impact when samples exceeded 5000 subjects. In an era of big data and artificial intelligence, it is tantalizing to explore new approaches. While we do not hasten exploration, our findings demonstrate that sample size trumps algorithm type. Agencies and providers should consider this finding when adopting or developing tools, as algorithms that offer transparency may also be top performers. (Publisher Abstract Provided)
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