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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2401.17062 |
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| _version_ | 1866917699179773952 |
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| author | Wiesbrock, Hans-Werner Großmann, Jürgen |
| author_facet | Wiesbrock, Hans-Werner Großmann, Jürgen |
| contents | This article proposes a test procedure that can be used to test ML models and ML-based systems independently of the actual training process. In this way, the typical quality statements such as accuracy and precision of these models and system can be verified independently, taking into account their black box character and the immanent stochastic properties of ML models and their training data. The article presents first results from a set of test experiments and suggest extensions to existing test methods reflecting the stochastic nature of ML models and ML-based systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17062 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Outline of an Independent Systematic Blackbox Test for ML-based Systems Wiesbrock, Hans-Werner Großmann, Jürgen Machine Learning This article proposes a test procedure that can be used to test ML models and ML-based systems independently of the actual training process. In this way, the typical quality statements such as accuracy and precision of these models and system can be verified independently, taking into account their black box character and the immanent stochastic properties of ML models and their training data. The article presents first results from a set of test experiments and suggest extensions to existing test methods reflecting the stochastic nature of ML models and ML-based systems. |
| title | Outline of an Independent Systematic Blackbox Test for ML-based Systems |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.17062 |