LMEMs for post-hoc analysis of HPO Benchmarking
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929449674473472 |
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| author | Geburek, Anton Mallik, Neeratyoy Stoll, Danny Bouthillier, Xavier Hutter, Frank |
| author_facet | Geburek, Anton Mallik, Neeratyoy Stoll, Danny Bouthillier, Xavier Hutter, Frank |
| contents | The importance of tuning hyperparameters in Machine Learning (ML) and Deep Learning (DL) is established through empirical research and applications, evident from the increase in new hyperparameter optimization (HPO) algorithms and benchmarks steadily added by the community. However, current benchmarking practices using averaged performance across many datasets may obscure key differences between HPO methods, especially for pairwise comparisons. In this work, we apply Linear Mixed-Effect Models-based (LMEMs) significance testing for post-hoc analysis of HPO benchmarking runs. LMEMs allow flexible and expressive modeling on the entire experiment data, including information such as benchmark meta-features, offering deeper insights than current analysis practices. We demonstrate this through a case study on the PriorBand paper's experiment data to find insights not reported in the original work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02533 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LMEMs for post-hoc analysis of HPO Benchmarking Geburek, Anton Mallik, Neeratyoy Stoll, Danny Bouthillier, Xavier Hutter, Frank Machine Learning The importance of tuning hyperparameters in Machine Learning (ML) and Deep Learning (DL) is established through empirical research and applications, evident from the increase in new hyperparameter optimization (HPO) algorithms and benchmarks steadily added by the community. However, current benchmarking practices using averaged performance across many datasets may obscure key differences between HPO methods, especially for pairwise comparisons. In this work, we apply Linear Mixed-Effect Models-based (LMEMs) significance testing for post-hoc analysis of HPO benchmarking runs. LMEMs allow flexible and expressive modeling on the entire experiment data, including information such as benchmark meta-features, offering deeper insights than current analysis practices. We demonstrate this through a case study on the PriorBand paper's experiment data to find insights not reported in the original work. |
| title | LMEMs for post-hoc analysis of HPO Benchmarking |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.02533 |