Design Principles for Falsifiable, Replicable and Reproducible Empirical ML Research
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913366927212544 |
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| author | Vranješ, Daniel Niggemann, Oliver |
| author_facet | Vranješ, Daniel Niggemann, Oliver |
| contents | Empirical research plays a fundamental role in the machine learning domain. At the heart of impactful empirical research lies the development of clear research hypotheses, which then shape the design of experiments. The execution of experiments must be carried out with precision to ensure reliable results, followed by statistical analysis to interpret these outcomes. This process is key to either supporting or refuting initial hypotheses. Despite its importance, there is a high variability in research practices across the machine learning community and no uniform understanding of quality criteria for empirical research. To address this gap, we propose a model for the empirical research process, accompanied by guidelines to uphold the validity of empirical research. By embracing these recommendations, greater consistency, enhanced reliability and increased impact can be achieved. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18077 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Design Principles for Falsifiable, Replicable and Reproducible Empirical ML Research Vranješ, Daniel Niggemann, Oliver Machine Learning Artificial Intelligence Empirical research plays a fundamental role in the machine learning domain. At the heart of impactful empirical research lies the development of clear research hypotheses, which then shape the design of experiments. The execution of experiments must be carried out with precision to ensure reliable results, followed by statistical analysis to interpret these outcomes. This process is key to either supporting or refuting initial hypotheses. Despite its importance, there is a high variability in research practices across the machine learning community and no uniform understanding of quality criteria for empirical research. To address this gap, we propose a model for the empirical research process, accompanied by guidelines to uphold the validity of empirical research. By embracing these recommendations, greater consistency, enhanced reliability and increased impact can be achieved. |
| title | Design Principles for Falsifiable, Replicable and Reproducible Empirical ML Research |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2405.18077 |