Machine learning as an experimental science
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Feature selection does not always improve the model's accuracy.
“Nonetheless, because ML is an experimental science, it is not always ensured to improve the model's accuracy through the feature selection step [55].”
From Cost Overrun Risk Assessment and Prediction in Construction Projects: A Bayesian Network Classifier Approach (2022) · cites this paper as [55]
In “Dependent measures of learning” (page unavailable), the paper says that plausible learning methods can worsen performance. “Varying the learning method” (page unavailable) also says a component may fail to aid learning. These passages support the general caution, but neither addresses feature selection specifically or shows its effect on model accuracy. (LLM, conf=0.78)
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- Authors
- Langley, P.
- Venue
- Machine Learning
- Published
- 1988-08
- Volume
- 3
- Issue
- 1
- Pages
- 5-8
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2400138769
- Cited by
- 187
- References
- 9
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 5 · 2023: 5 · 2022: 5 · 2021: 8 · 2020: 7 · 2019: 6 · 2018: 7 · 2017: 5 · 2016: 4 · 2015: 11 · 2014: 6 · 2013: 3 · 2012: 9
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- experimental science, scientific method, reproducibility, hypothesis testing
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