- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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1 Online-Ressource.
- Language
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Englisch
- Bibliographic citation
-
Trimming stability selection increases variable selection robustness ; volume:112 ; number:12 ; day:4 ; month:10 ; year:2023 ; pages:4995-5055 ; date:12.2023
Machine learning ; 112, Heft 12 (4.10.2023), 4995-5055, 12.2023
- Creator
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Werner, Tino
- Contributor
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SpringerLink (Online service)
- DOI
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10.1007/s10994-023-06384-z
- URN
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urn:nbn:de:101:1-2024013111465991557774
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:32 AM CEST
Data provider
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Associated
- Werner, Tino
- SpringerLink (Online service)
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Robustness of Random Forest-based gene selection methods
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Using reference models in variable selection
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The robustness of model selection rules
Regularization and variable selection in Heckman selection model
Loss-guided stability selection
Selection of variables in small business failure analysis: Mean selection vs. median selection
Robustness of Random Forest-based gene selection methods
Bootstrap Variable-Selection and Confidence Sets
Using reference models in variable selection
Efficient selection rules to increase non-linear merit: application in mate selection
Forward stability and model path selection
Simultaneous clustering and variable selection: A novel algorithm and model selection procedure