Explaining Hyperparameter Optimization via Partial Dependence Plots
This work addresses the problem of trust and understanding in automated hyperparameter optimization for machine learning practitioners, but it is incremental as it builds on existing methods.
The paper tackles the lack of explainability in hyperparameter optimization by using interpretable machine learning to analyze Bayesian optimization data, introducing a variant of partial dependence plots with confidence bands to mitigate sampling bias, and showing quantitative evidence of improved plot quality in sub-regions.
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 regions with potential high-performance configurations and thus induces a sampling bias. Hence, many IML techniques, such as the partial dependence plot (PDP), carry the risk of generating biased interpretations. By leveraging the posterior uncertainty of the BO surrogate model, we introduce a variant of the PDP with estimated confidence bands. We propose to partition the hyperparameter space to obtain more confident and reliable PDPs in relevant sub-regions. In an experimental study, we provide quantitative evidence for the increased quality of the PDPs within sub-regions.