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Explaining Hyperparameter Optimization via Partial Dependence Plots. (arXiv:2111.04820v2 [cs.LG] UPDATED)
Jan. 27, 2022, 2:11 a.m. | Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl
cs.LG updates on arXiv.org arxiv.org
Automated hyperparameter optimization (HPO) can support practitioners to
obtain peak performance in machine learning models. However, there is often a
lack of valuable insights into the effects of different hyperparameters on the
final model performance. This lack of explainability makes it difficult to
trust and understand the automated HPO process and its results. We suggest
using interpretable machine learning (IML) to gain insights from the
experimental data obtained during HPO with Bayesian optimization (BO). BO tends
to focus on promising …
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