Results 11 to 20 of about 3,583,799 (330)
Area under the ROC Curve has the most consistent evaluation for binary classification [PDF]
PLoS ONEThe proper use of model evaluation metrics is important for model evaluation and model selection in binary classification tasks. This study investigates how consistent different metrics are at evaluating models across data of different prevalence while ...
Jing Li
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A relationship between the incremental values of area under the ROC curve and of area under the precision-recall curve [PDF]
Diagnostic and Prognostic Research, 2021 Background Incremental value (IncV) evaluates the performance change between an existing risk model and a new model. Different IncV metrics do not always agree with each other. For example, compared with a prescribed-dose model, an ovarian-dose model for
Qian M. Zhou +4 more
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Semergen, 2022 ROC-curve of the conditional logistic model showing an excellent [19] prediction of cardiovascular related death (AUC = 0.846). The scale is described by Mandrekar [19] as follows: 0 indicates a perfectly inaccurate test, an AUC of 0.5 suggests no ...
J. M. Martínez Pérez +1 more
semanticscholar +5 more sources
Induction Motor Fault Classification Based on ROC Curve and t-SNE
IEEE Access, 2021 This paper proposes a novel fault classification method with application to induction motors, which is based on integrating and combining with receiver operating characteristic (ROC) curve and t-distribution stochastic neighbor embedding (t-SNE ...
Chun-Yao Lee, Wen-Cheng Lin
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The use of the area under the ROC curve in the evaluation of machine learning algorithms
Pattern Recognition, 1997 In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms.
Andrew P Bradley
exaly +2 more sources
Smooth ROC curve estimation via Bernstein polynomials. [PDF]
PLoS ONE, 2021 The receiver operating characteristic (ROC) curve is commonly used to evaluate the accuracy of a diagnostic test for classifying observations into two groups.
Dongliang Wang, Xueya Cai
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Model-Based ROC Curve: Examining the Effect of Case Mix and Model Calibration on the ROC Plot. [PDF]
Med Decis Making, 2022 Background The performance of risk prediction models is often characterized in terms of discrimination and calibration. The receiver-operating characteristic (ROC) curve is widely used for evaluating model discrimination.
Sadatsafavi M +2 more
europepmc +3 more sources
Time-dependent ROC curve analysis in medical research: current methods and applications
BMC Medical Research Methodology, 2017 Background ROC (receiver operating characteristic) curve analysis is well established for assessing how well a marker is capable of discriminating between individuals who experience disease onset and individuals who do not.
Adina Najwa Kamarudin +2 more
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ROC curve analysis: a useful statistic multi-tool in the research of nephrology. [PDF]
Int Urol NephrolIn the past decade, scientific research in the area of Nephrology has focused on evaluating the clinical utility and performance of various biomarkers for diagnosis, risk stratification and prognosis.
Roumeliotis S +8 more
europepmc +2 more sources
Semergen, 2023 The ROC curve is a statistical tool used to evaluate the discriminative capacity of a dichotomous diagnostic test. These are curves in which sensitivity is presented as a function of false positives (complementary to specificity) for different cut-off points.
J A, Martínez Pérez +1 more
core +4 more sources