Practitioner Motives to Use Different Hyperparameter Optimization Methods
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866918275735093248 |
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| author | Kannengießer, Niclas Hasebrook, Niklas Morsbach, Felix Zöller, Marc-André Franke, Jörg Lindauer, Marius Hutter, Frank Sunyaev, Ali |
| author_facet | Kannengießer, Niclas Hasebrook, Niklas Morsbach, Felix Zöller, Marc-André Franke, Jörg Lindauer, Marius Hutter, Frank Sunyaev, Ali |
| contents | Programmatic hyperparameter optimization (HPO) methods, such as Bayesian optimization and evolutionary algorithms, are highly sample-efficient in identifying optimal hyperparameter configurations for machine learning (ML) models. However, practitioners frequently use less efficient methods, such as grid search, which can lead to under-optimized models. We suspect this behavior is driven by a range of practitioner-specific motives. Practitioner motives, however, still need to be clarified to enhance user-centered development of HPO tools. To uncover practitioner motives to use different HPO methods, we conducted 20 semi-structured interviews and an online survey with 49 ML experts. By presenting main goals (e.g., increase ML model understanding) and contextual factors affecting practitioners' selection of HPO methods (e.g., available computer resources), this study offers a conceptual foundation to better understand why practitioners use different HPO methods, supporting development of more user-centered and context-adaptive HPO tools in automated ML. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2203_01717 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Practitioner Motives to Use Different Hyperparameter Optimization Methods Kannengießer, Niclas Hasebrook, Niklas Morsbach, Felix Zöller, Marc-André Franke, Jörg Lindauer, Marius Hutter, Frank Sunyaev, Ali Machine Learning Programmatic hyperparameter optimization (HPO) methods, such as Bayesian optimization and evolutionary algorithms, are highly sample-efficient in identifying optimal hyperparameter configurations for machine learning (ML) models. However, practitioners frequently use less efficient methods, such as grid search, which can lead to under-optimized models. We suspect this behavior is driven by a range of practitioner-specific motives. Practitioner motives, however, still need to be clarified to enhance user-centered development of HPO tools. To uncover practitioner motives to use different HPO methods, we conducted 20 semi-structured interviews and an online survey with 49 ML experts. By presenting main goals (e.g., increase ML model understanding) and contextual factors affecting practitioners' selection of HPO methods (e.g., available computer resources), this study offers a conceptual foundation to better understand why practitioners use different HPO methods, supporting development of more user-centered and context-adaptive HPO tools in automated ML. |
| title | Practitioner Motives to Use Different Hyperparameter Optimization Methods |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2203.01717 |