ROC and AUC with a Binary Predictor: a Potentially Misleading Metric.
R
area under the curve
auc
roc
Journal
Journal of classification
ISSN: 0176-4268
Titre abrégé: J Classif
Pays: United States
ID NLM: 9882909
Informations de publication
Date de publication:
Oct 2020
Oct 2020
Historique:
entrez:
30
11
2020
pubmed:
1
12
2020
medline:
1
12
2020
Statut:
ppublish
Résumé
In analysis of binary outcomes, the receiver operator characteristic (ROC) curve is heavily used to show the performance of a model or algorithm. The ROC curve is informative about the performance over a series of thresholds and can be summarized by the area under the curve (AUC), a single number. When a
Identifiants
pubmed: 33250548
doi: 10.1007/s00357-019-09345-1
pmc: PMC7695228
mid: NIHMS1547357
doi:
Types de publication
Journal Article
Langues
eng
Pagination
696-708Subventions
Organisme : NINDS NIH HHS
ID : R01 NS060910
Pays : United States
Organisme : NINDS NIH HHS
ID : U01 NS080824
Pays : United States
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