Add glossary term: Data Leakage
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Assessment
- Difficulty
- 1/5
- Estimated time
- Under an hour
- Newbie friendliness
- 88/100
- Issue type
- Documentation
- Clarity
- Clearly specified
- Activity status
- Quiet
- Tech stack
- scikit-learn
- Domain
- documentation, machine-learning
Research direction
Edit docs/wiki-guide/Glossary-for-Imageomics.md and first review nearby glossary entries for formatting and level of detail. Add the proposed Data Leakage term with its Imageomics-specific context and references, then check the rendered Markdown and links. Done means the glossary entry is clear, consistent with surrounding terms, and correctly cited.
Written by the indexing model from the issue text.
Description
Proposed glossary item
Data Leakage
In machine learning, data leakage occurs when information that would not be available at prediction time influences model building or evaluation, yielding overly optimistic performance estimates. In Imageomics datasets, leakage can occur when duplicate images or multiple views or records of the same biological specimen are divided between training and evaluation; keep all records with the same specimen identifier in one split.
This proposed definition is based on the Imageomics data-workshop onboarding lesson and is intended for docs/wiki-guide/Glossary-for-Imageomics.md.
References:
- Dominant language
- Python
- Stars
- 6
- Forks
- 0
- Avg merge
- 1h 28m
- Merged PRs (30d)
- 3
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