Results 191 to 200 of about 454,593 (211)

On the bias in the AUC variance estimate

Pattern Recognition Letters
The area under the Receiver Operating Characteristic (ROC) curve (AUC) is a standard metric for quantifying and comparing binary classifiers. A popular approach to estimating the AUCs and the associated variabilities - the variance of the AUC or the full covariance matrix of multiple correlated AUCs - is the one proposed by DeLong et al [1], which is ...
openaire   +2 more sources

Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling

Global Ecology and Biogeography, 2012
Alberto Jimenez-Valverde
exaly  

AUC: a misleading measure of the performance of predictive distribution models

Global Ecology and Biogeography, 2008
Jorge M Lobo, Alberto Jimenez-Valverde, Raimundo Real   +2 more
exaly  

Using AUC and accuracy in evaluating learning algorithms

IEEE Transactions on Knowledge and Data Engineering, 2005
C X Ling
exaly  

AUC-Maximizing Ensembles through Metalearning

International Journal of Biostatistics, 2016
Mark J Van Der Laan, Maya Petersen
exaly  

An alternative approach to calculating Area‐Under‐the‐Curve (AUC) in delay discounting research

Journal of the Experimental Analysis of Behavior, 2016
Allison M Borges, Jinyi Kuang, Richard Yi   +2 more
exaly  

AUC

2013
openaire   +1 more source

One-Shot Estimate of MRMC Variance: AUC

Academic Radiology, 2006
Brandon D Gallas
exaly  

AUC 2019

2021
openaire   +1 more source

Half-AUC for the evaluation of sensitive or specific classifiers

Pattern Recognition Letters, 2014
Andrew P Bradley
exaly