Year: 2018
Type: conference-paper
Abstract: Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. While widely applied in empirical Software Engineering, there has not been much discussion on which hyperparameter tuner is best for software analytics.To address this gap in the literature, this paper applied a range of hyperparameter optimizers (grid search, random search, differential evolution, and Bayesian optimization) to a defect prediction problem. Surprisingly, no hyperparameter optimizer was observed to be “best” and, for one of the two evaluation measures studied here (F-measure), hyperparameter optimization, in 50% of cases, was no better than using default configurations.
Authors: Huy Tu, Vivek Nair
Institution: North Carolina State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2883743081