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Leakage (machine learning) (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Leakage (machine learning)" in English language version.

refsWebsite
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2doi.org
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2nih.gov
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1researchgate.net
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1semanticscholar.org
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1youtube.com
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1aiukraine.com
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1twitter.com
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1web.archive.org
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1worldcat.org
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1shelf.io
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1ibm.com
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aiukraine.com

doi.org

  • Shachar Kaufman; Saharon Rosset; Claudia Perlich (January 2011). "Leakage in data mining: Formulation, detection, and avoidance". Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. Vol. 6. pp. 556–563. doi:10.1145/2020408.2020496. ISBN 9781450308137. S2CID 9168804. Retrieved 13 January 2020.
  • Kapoor, Sayash; Narayanan, Arvind (August 2023). "Leakage and the reproducibility crisis in machine-learning-based science". Patterns. 4 (9): 100804. doi:10.1016/j.patter.2023.100804. ISSN 2666-3899. PMC 10499856. PMID 37720327.

ibm.com

nih.gov

ncbi.nlm.nih.gov

pubmed.ncbi.nlm.nih.gov

researchgate.net

  • Shachar Kaufman; Saharon Rosset; Claudia Perlich (January 2011). "Leakage in data mining: Formulation, detection, and avoidance". Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. Vol. 6. pp. 556–563. doi:10.1145/2020408.2020496. ISBN 9781450308137. S2CID 9168804. Retrieved 13 January 2020.

semanticscholar.org

api.semanticscholar.org

  • Shachar Kaufman; Saharon Rosset; Claudia Perlich (January 2011). "Leakage in data mining: Formulation, detection, and avoidance". Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. Vol. 6. pp. 556–563. doi:10.1145/2020408.2020496. ISBN 9781450308137. S2CID 9168804. Retrieved 13 January 2020.

shelf.io

twitter.com

  • Nick, Roberts (16 November 2017). "Replying to @AndrewYNg @pranavrajpurkar and 2 others". Brooklyn, NY, USA: Twitter. Archived from the original on 10 June 2018. Retrieved 13 January 2020. Replying to @AndrewYNg @pranavrajpurkar and 2 others ... Were you concerned that the network could memorize patient anatomy since patients cross train and validation? "ChestX-ray14 dataset contains 112,120 frontal-view X-ray images of 30,805 unique patients. We randomly split the entire dataset into 80% training, and 20% validation."

web.archive.org

  • Nick, Roberts (16 November 2017). "Replying to @AndrewYNg @pranavrajpurkar and 2 others". Brooklyn, NY, USA: Twitter. Archived from the original on 10 June 2018. Retrieved 13 January 2020. Replying to @AndrewYNg @pranavrajpurkar and 2 others ... Were you concerned that the network could memorize patient anatomy since patients cross train and validation? "ChestX-ray14 dataset contains 112,120 frontal-view X-ray images of 30,805 unique patients. We randomly split the entire dataset into 80% training, and 20% validation."

worldcat.org

search.worldcat.org

youtube.com