Learning to Tune XGBoost with XGBoost

Johanna Sommer; Dimitrios Sarigiannis; Thomas Parnell

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arXiv · 1909.07218

Learning to Tune XGBoost with XGBoost

Johanna Sommer·Dimitrios Sarigiannis·Thomas Parnell

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Abstract

In this short paper we investigate whether meta-learning techniques can be used to more effectively tune the hyperparameters of machine learning models using successive halving (SH). We propose a novel variant of the SH algorithm (MeSH), that uses meta-regressors to determine which candidate configurations should be eliminated at each round. We apply MeSH to the problem of tuning the hyperparameters of a gradient-boosted decision tree model. By training and tuning our meta-regressors using existing tuning jobs from 95 datasets, we demonstrate that MeSH can often find a superior solution to both SH and random search.

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Johanna Sommer, Dimitrios Sarigiannis, Thomas Parnell. 2019-09-16. Learning to Tune XGBoost with XGBoost. https://arxiv.org/abs/1909.07218

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