GPU-Accelerated Parameter Optimization for Classification Rule Learning

Abstract

While some studies comparing rule-based classifiers enumerate a parameter over several values, most use all default values, presumably due to the high computational cost of jointly tuning multiple parameters. We show that thorough, joint optimization of search parameters on individual datasets gives higher out-ofsample precision than fixed baselines. We… (More)

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