An Empirical Study on the Usage of Automated Machine Learning Tools Article Swipe
Forough Majidi
,
Moses Openja
,
Foutse Khomh
,
Heng Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2208.13116
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2208.13116
The popularity of automated machine learning (AutoML) tools in different domains has increased over the past few years. Machine learning (ML) practitioners use AutoML tools to automate and optimize the process of feature engineering, model training, and hyperparameter optimization and so on. Recent work performed qualitative studies on practitioners' experiences of using AutoML tools and compared different AutoML tools based on their performance and provided features, but none of the existing work studied the practices of using AutoML tools in real-world projects at a large scale. Therefore, we conducted an empirical study to understand how ML practitioners use AutoML tools in their projects. To this end, we examined the top 10 most used AutoML tools and their respective usages in a large number of open-source project repositories hosted on GitHub. The results of our study show 1) which AutoML tools are mostly used by ML practitioners and 2) the characteristics of the repositories that use these AutoML tools. Also, we identified the purpose of using AutoML tools (e.g. model parameter sampling, search space management, model evaluation/error-analysis, Data/ feature transformation, and data labeling) and the stages of the ML pipeline (e.g. feature engineering) where AutoML tools are used. Finally, we report how often AutoML tools are used together in the same source code files. We hope our results can help ML practitioners learn about different AutoML tools and their usages, so that they can pick the right tool for their purposes. Besides, AutoML tool developers can benefit from our findings to gain insight into the usages of their tools and improve their tools to better fit the users' usages and needs.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2208.13116
- https://arxiv.org/pdf/2208.13116
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https://openalex.org/W4293790009Canonical identifier for this work in OpenAlex
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https://doi.org/10.48550/arxiv.2208.13116Digital Object Identifier
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An Empirical Study on the Usage of Automated Machine Learning ToolsWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-08-28Full publication date if available
- Authors
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Forough Majidi, Moses Openja, Foutse Khomh, Heng LiList of authors in order
- Landing page
-
https://arxiv.org/abs/2208.13116Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2208.13116Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2208.13116Direct OA link when available
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.understand | 93 |
| abstract_inverted_index.experiences | 49 |
| abstract_inverted_index.management, | 173 |
| abstract_inverted_index.open-source | 124 |
| abstract_inverted_index.performance | 62 |
| abstract_inverted_index.qualitative | 45 |
| abstract_inverted_index.engineering) | 191 |
| abstract_inverted_index.engineering, | 33 |
| abstract_inverted_index.optimization | 38 |
| abstract_inverted_index.repositories | 126, 152 |
| abstract_inverted_index.practitioners | 21, 96, 145, 220 |
| abstract_inverted_index.hyperparameter | 37 |
| abstract_inverted_index.practitioners' | 48 |
| abstract_inverted_index.characteristics | 149 |
| abstract_inverted_index.transformation, | 178 |
| abstract_inverted_index.evaluation/error-analysis, | 175 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.49000000953674316 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |