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Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates
In binary classification problems, the area under the ROC curve (AUC) is commonly used to evaluate the performance of a prediction model. Often, it is combined with cross-validation in order to assess how the results will generalize to an independent data set. In order to evaluate the quality of an...
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| Veröffentlicht in: | Electron J Stat |
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| Hauptverfasser: | , , |
| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
2015
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| Schlagworte: | |
| Online Zugang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4533123/ https://ncbi.nlm.nih.gov/pubmed/26279737 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1214/15-EJS1035 |
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