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arXiv · 2101.02289

Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models

Jeroen van Hoof·Joaquin Vanschoren
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Abstract

Bayesian Optimization is a popular tool for tuning algorithms in automatic machine learning (AutoML) systems. Current state-of-the-art methods leverage Random Forests or Gaussian processes to build a surrogate model that predicts algorithm performance given a certain set of hyperparameter settings. In this paper, we propose a new surrogate model based on gradient boosting, where we use quantile regression to provide optimistic estimates of the performance of an unobserved hyperparameter setting, and combine this with a distance metric between unobserved and observed hyperparameter settings to help regulate exploration. We demonstrate empirically that the new method is able to outperform some state-of-the art techniques across a reasonable sized set of classification problems.

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BibTeXRIS

Jeroen van Hoof, Joaquin Vanschoren. 2021-01-06. Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models. https://arxiv.org/abs/2101.02289

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