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Forecasts of prospective criminal behavior have long been an importantfeature of many criminal justice decisions. There is now substantial evidencethat machine learning procedures will classify and forecast at least as well,and typically better, than logistic regression, which has to date dominatedconventional practice. However, machine learning procedures are adaptive. They"learn" inductively from training data. As a result, they typically performbest with very large datasets. There is a need, therefore, for forecastingprocedures with the promise of machine learning that will perform well withsmall to moderately-sized datasets. Kernel methods provide precisely thatpromise. In this paper, we offer an overview of kernel methods in regressionsettings and compare such a method, regularized with principle components, tostepwise logistic regression. We apply both to a timely and important criminaljustice concern: a failure to appear (FTA) at court proceedings following anarraignment. A forecast of an FTA can be an important factor is a judge'sdecision to release a defendant while awaiting trial and can influence theconditions imposed on that release. Forecasting accuracy matters, and ourkernel approach forecasts far more accurately than stepwise logisticregression. The methods developed here are implemented in the R package kernRegcurrently available on CRAN.

Using Regression Kernels to Forecast A Failure to Appear in Court