Information leakage
Last revised by Yusra Sheikh on 2 Aug 2021
Citation, DOI, disclosures and article data
Citation:
Botz B, Sheikh Y, Information leakage. Reference article, Radiopaedia.org (Accessed on 25 Apr 2024) https://doi.org/10.53347/rID-83268
Permalink:
rID:
83268
Article created:
18 Oct 2020,
Bálint Botz ◉
Disclosures:
At the time the article was created Bálint Botz had no recorded disclosures.
View Bálint Botz's current disclosures
Last revised:
2 Aug 2021,
Yusra Sheikh ◉
Disclosures:
At the time the article was last revised Yusra Sheikh had no recorded disclosures.
View Yusra Sheikh's current disclosures
Revisions:
2 times, by
2 contributors -
see full revision history and disclosures
Sections:
Information leakage is one of the common and important errors in data handling during all machine learning applications, including those in radiology. Briefly, it means the incomplete separation of the training, validation, and testing datasets, which can significantly change the apparent performance of the algorithmic method.
Since data overlap between datasets is a critical biassing factor, it is crucial to split data at the beginning of the study, before proceeding with any further steps (e.g. feature extraction) as these can result in data leakage 1.
References
- 1. Burak Kocak, Ece Ates Kus, Ozgur Kilickesmez. How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts. (2020) European Radiology. doi:10.1007/s00330-020-07324-4 - Pubmed
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