Results 31 to 40 of about 3,627,888 (206)
EntropyPerformance metrics are measures of success or performance that can be used to evaluate how well a model makes accurate predictions or classifications.
Aylin Gocoglu +2 more
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A Linear Regression Framework for the Receiver Operating Characteristic(ROC) Curve Analysis [PDF]
Journal of Biometrics & Biostatistics, 2012 The receiver operating characteristic (ROC) curve has been a popular statistical tool for characterizing the discriminating power of a classifier, such as a biomarker or an imaging modality for disease screening or diagnosis. It has been recognized that the accuracy of a given procedure may depend on some underlying factors, such as subject's ...
Zhang, Zheng, Huang, Ying
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Caspian Journal of Neurological Sciences, 2020 Background: Psychiatrists use different scales to evaluate post-stroke depression; however, some concerns have raised about their low specificity. Objectives: This study aimed to assess the validity and reliability of the Persian version of the Post ...
Somayeh Shokrgozar +5 more
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Acceptance sampling for attributes via hypothesis testing and the hypergeometric distribution
Journal of Industrial Engineering International, 2017 This paper questions some aspects of attribute acceptance sampling in light of the original concepts of hypothesis testing from Neyman and Pearson (NP).
Robert Wayne Samohyl
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Minimum‐Norm Estimation for Binormal Receiver Operating Characteristic (ROC) Curves [PDF]
Biometrical Journal, 2009 AbstractThe receiver operating characteristic (ROC) curve is often used to assess the usefulness of a diagnostic test. We present a new method to estimate the parameters of a popular semi‐parametric ROC model, called the binormal model. Our method is based on minimization of the functional distance between two estimators of an unknown transformation ...
Ori, Davidov, Yuval, Nov
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Applied Medical Informatics, 2023 Combining biomarkers and their statistics is used to increase the prediction performance of a diagnosis, but no gold standard method exists. We introduced and evaluated an approach using linear combinations of summary-based statistics tested in logistic ...
Ilie-Andrei CONDURACHE +1 more
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Receiver Operating Characteristic (ROC) Curves
, 2018 Receiver operating characteristic (ROC) curves are used ubiquitously to evaluate covariates, markers, or features as potential predictors in binary problems. We distinguish raw ROC diagnostics and ROC curves, elucidate the special role of concavity in interpreting and modelling ROC curves, and establish an equivalence between ROC curves and cumulative ...
Gneiting, Tilmann, Vogel, Peter
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ROCS: receiver operating characteristic surface for class-skewed high-throughput data. [PDF]
PLoS ONE, 2012 The receiver operating characteristic (ROC) curve is an important tool to gauge the performance of classifiers. In certain situations of high-throughput data analysis, the data is heavily class-skewed, i.e.
Tianwei Yu
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A Modified AUC for Training Convolutional Neural Networks: Taking Confidence Into Account
Frontiers in Artificial Intelligence, 2021 Receiver operating characteristic (ROC) curve is an informative tool in binary classification and Area Under ROC Curve (AUC) is a popular metric for reporting performance of binary classifiers.
Khashayar Namdar +5 more
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Vaccines, 2022 Background: The viral neutralization assay is the gold standard to estimate the level of immunity against SARS-CoV-2. This study analyzes the correlation between the quantitative Anti-SARS-CoV-2 QuantiVac ELISA (IgG) and the NeutraLISA neutralization ...
Engy Mohamed El-Ghitany +3 more
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