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Hyperparameter Optimization Across Problem Tasks

Schilling, Nicolas; Windler, Torben; Schmidt-Thieme, Lars

Abstract:
Hyperparameter Optimization is a task that is generally hard to accomplish as the correct setting of hyperparameters cannot be learned from the data directly. However, finding the right hyperparameters is necessary as the performance on test data can differ a lot under various hyperparameter settings. Many researchers rely on search techniques such as grid-search, having the downside that they require a lot of computation time, as prediction models are learned for a wide range of possible hyperparameter configurations which is only feasible in a parallel computing environment. Recently, search methods based on Bayesian optimization such as SMAC have been proposed and extended to include hyperparameter performance of the same model on another data set. These meta learning approaches show that the search for well-performing hyperparameters can be steered in a more intelligent manner. In this work, we aim to accomplish hyperparameter optimization across problem tasks where we specifically target regression and classification problems. We show, that the incorporation of hyperparameter performance on a classification task is helpful when optimizing hyperparameters for a regression task and vice versa.

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Verlagsausgabe §
DOI: 10.5445/KSP/1000085951/14
Veröffentlicht am 23.08.2019
Coverbild
Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Jahr 2018
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000097744
Erschienen in Archives of Data Science, Series A (Online First)
Band 4
Heft 1
Seiten A14, 19 S. online
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