- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1471-2105
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
An AUC-based permutation variable importance measure for random forests ; volume:14 ; number:1 ; day:5 ; month:4 ; year:2013 ; pages:1-11 ; date:12.2013
BMC bioinformatics ; 14, Heft 1 (5.4.2013), 1-11, 12.2013
- Creator
-
Janitza, Silke
- Contributor
- DOI
-
10.1186/1471-2105-14-119
- URN
-
urn:nbn:de:1111-2016021739722
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:21 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Janitza, Silke
- Strobl, Carolin
- Boulesteix, Anne-Laure
- SpringerLink (Online service)
Other Objects (12)
Maintaining AUC and H-measure over time
Conditional permutation importance revisited
Enhancing Network Threat Detection with Random Forest-Based NIDS and Permutation Feature Importance
The behaviour of random forest permutation-based variable importance measures under predictor correlation
Development of MWL-AUC/CCD-C-AUC/SLS-AUC detectors for the analytical ultracentrifuge
Stuttgart Auc-Aur
Negation of permutation mass function in random permutation sets theory for uncertain information modeling
Intervention in prediction measure: a new approach to assessing variable importance for random forests
Cross‐validated permutation feature importance considering correlation between features
AUC Politica 3/2022
The Open AUC Project
Cutoff for conjugacy-invariant random walks on the permutation group
Maintaining AUC and H-measure over time
Conditional permutation importance revisited
Enhancing Network Threat Detection with Random Forest-Based NIDS and Permutation Feature Importance
The behaviour of random forest permutation-based variable importance measures under predictor correlation
Development of MWL-AUC/CCD-C-AUC/SLS-AUC detectors for the analytical ultracentrifuge
Stuttgart Auc-Aur
Negation of permutation mass function in random permutation sets theory for uncertain information modeling
Intervention in prediction measure: a new approach to assessing variable importance for random forests
Cross‐validated permutation feature importance considering correlation between features
AUC Politica 3/2022
The Open AUC Project
Cutoff for conjugacy-invariant random walks on the permutation group
Maintaining AUC and H-measure over time
Conditional permutation importance revisited
Enhancing Network Threat Detection with Random Forest-Based NIDS and Permutation Feature Importance
The behaviour of random forest permutation-based variable importance measures under predictor correlation
Development of MWL-AUC/CCD-C-AUC/SLS-AUC detectors for the analytical ultracentrifuge
Stuttgart Auc-Aur
Negation of permutation mass function in random permutation sets theory for uncertain information modeling
Intervention in prediction measure: a new approach to assessing variable importance for random forests
Cross‐validated permutation feature importance considering correlation between features
AUC Politica 3/2022
The Open AUC Project