Overfitting Avoidance as Bias
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The most common interpretation of an Occamist bias is that amongst all hypotheses consistent with the data, the simplest is the most likely.
“The most common interpretation of an Occamist bias is that amongst all hypotheses consistent with the data, the simplest is the most likely 28 .”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Schaffer, 1993
Section 8.1.1 (page unavailable) says Occam’s statement has sometimes been construed to mean that, other things being equal, simple hypotheses are more likely to be predictive. It does not call this the most common interpretation, and it discusses predictive performance rather than the likelihood that the simplest data-consistent hypothesis is true. (LLM, conf=0.84)
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- Authors
- Schaffer, C.
- Affiliations
- City University of New York (US)
- Corresponding author
- Schaffer, Cullen
- Venue
- Machine Learning
- Published
- 1993-02
- Volume
- 10
- Issue
- 2
- Pages
- 153-178
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1995023359
- Cited by
- 310
- References
- 29
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 10 · 2023: 12 · 2022: 12 · 2021: 29 · 2020: 19 · 2019: 11 · 2018: 6 · 2017: 7 · 2016: 5 · 2015: 3 · 2014: 2 · 2013: 1 · 2012: 5
- FWCI
- 62.52
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- overfitting avoidance, decision tree induction, selective pruning, bias-variance trade-off, prediction accuracy, generalization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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