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J. Hanley, B. McNeil (1982)The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Radiology, 143 1
(2011)Determinants of market price of shares of the select banking companies listed at bombay stock exchange
John Hunter (2007)Matplotlib: A 2D Graphics Environment
Computing in Science & Engineering, 9
Brian Snarr, Michael Liu, Jeremy Zuckerberg, C. Falkensammer, Sumekala Nadaraj, D. Burstein, Deborah Ho, Monique Gardner, A. Butto, S. Ewing, N. Pandian, A. Banerjee (2017)The Parasternal Short‐Axis View Improves Diagnostic Accuracy for Inferior Sinus Venosus Type of Atrial Septal Defects by Transthoracic Echocardiography
Journal of the American Society of Echocardiography, 30
D. Blumberg, C. Moraes, J. Liebmann, Reena Garg, Cynthia Chen, A. Theventhiran, D. Hood (2016)Technology and the Glaucoma Suspect
Investigative Ophthalmology & Visual Science, 57
E. DeLong, D. DeLong, D. Clarke‐Pearson (1988)Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.
Biometrics, 44 3
D. Bamber (1975)The area above the ordinal dominance graph and the area below the receiver operating characteristic graph
Journal of Mathematical Psychology, 12
M. Pepe, G. Longton, H. Janes (2009)Estimation and Comparison of Receiver Operating Characteristic Curves
The Stata Journal, 9
(2018)A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria
(2017)9.4 [computer program]
Xin Xiong, Qi Li, Wensong Yang, Xiao Wei, Xi Hu, Xing-Chen Wang, Dan Zhu, Rui Li, D. Cao, Peng Xie (2018)Comparison of Swirl Sign and Black Hole Sign in Predicting Early Hematoma Growth in Patients with Spontaneous Intracerebral Hemorrhage
Medical Science Monitor : International Medical Journal of Experimental and Clinical Research, 24
Taras Litvin, G. Bresnick, Jorge Cuadros, S. Selvin, Kuniyoshi Kanai, Glen Ozawa (2017)A Revised Approach for the Detection of Sight-Threatening Diabetic Macular Edema
JAMA Ophthalmology, 135
Yu‐Chin Hsu, Robert Lieli (2021)Inference for ROC Curves Based on Estimated Predictive Indices
X. Robin, N. Turck, A. Hainard, N. Tiberti, F. Lisacek, Jean-Charles Sanchez, Markus Müller (2011)pROC: an open-source package for R and S+ to analyze and compare ROC curves
BMC Bioinformatics, 12
Daniel Veltri, Uday Kamath, Amarda Shehu (2018)Deep learning improves antimicrobial peptide recognition
Joris Budweg, T. Sprenger, A. Vere-Tyndall, Anne Hagenkord, C. Stippich, C. Berger (2016)Factors associated with significant MRI findings in medical walk-in patients with acute headache.
Swiss medical weekly, 146
Fabian Pedregosa, G. Varoquaux, Alexandre Gramfort, V. Michel, B. Thirion, O. Grisel, Mathieu Blondel, Gilles Louppe, P. Prettenhofer, Ron Weiss, Ron Weiss, J. Vanderplas, Alexandre Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay (2011)Scikit-learn: Machine Learning in Python
F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, M Blondel (2011)Scikit-learn: machine learning in python
Journal of Machine Learning Research, 12
R. Team (2014)R: A language and environment for statistical computing.
MSOR connections, 1
V. Kushnir, S. Darmon, D. Barad, N. Gleicher (2018)Degree of mosaicism in trophectoderm does not predict pregnancy potential: a corrected analysis of pregnancy outcomes following transfer of mosaic embryos
Reproductive Biology and Endocrinology : RB&E, 16
Tom Fawcett (2006)An introduction to ROC analysis
Pattern Recognit. Lett., 27
Tobias Sing, Oliver Sander, N. Beerenwinkel, Thomas Lengauer (2005)ROCR: visualizing classifier performance in R
Bioinformatics, 21 20
(2018)reticulate: Interface to 'Python'. R package version 1
Ivo Shterev, D. Dunson, Cliburn Chan, G. Sempowski (2017)Bayesian Multi-Plate High-Throughput Screening of Compounds
Scientific Reports, 8
(2016)fbroc: Fast Algorithms to Bootstrap Receiver Operating Characteristics Curves. R package version 0.4.0
E. Maverakis, Chelsea Ma, K. Shinkai, D. Fiorentino, J. Callen, U. Wollina, A. Marzano, D. Wallach, Kyoungmi Kim, C. Schadt, A. Ormerod, M. Fung, A. Steel, F. Patel, Rosie Qin, F. Craig, H. Williams, F. Powell, A. Merleev, M. Cheng (2018)Diagnostic Criteria of Ulcerative Pyoderma Gangrenosum: A Delphi Consensus of International Experts
JAMA Dermatology, 154
B. Mwipatayi, Surabhi Sharma, A. Daneshmand, Shannon Thomas, V. Vijayan, N. Altaf, Marek Garbowski, M. Jackson (2016)Durability of the balloon-expandable covered versus bare-metal stents in the Covered versus Balloon Expandable Stent Trial (COBEST) for the treatment of aortoiliac occlusive disease.
Journal of vascular surgery, 64 1
(2018)caTools: Tools: moving window statistics, GIF, Base64, ROC AUC, etc. R package version 22.214.171.124
S. Glaveckaitė, N. Valevičienė, D. Palionis, V. Skorniakov, J. Čelutkienė, A. Tamošiūnas, G. Uždavinys, A. Laucevičius (2011)Value of scar imaging and inotropic reserve combination for the prediction of segmental and global left ventricular functional recovery after revascularisation
Journal of Cardiovascular Magnetic Resonance, 13
Takaya Saito, Marc Rehmsmeier (2015)The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets
PLoS ONE, 10
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 predictor is categorical, the ROC curve has one less than number of categories as potential thresholds; when the predictor is binary, there is only one threshold. As the AUC may be used in decision-making processes on determining the best model, it important to discuss how it agrees with the intuition from the ROC curve. We discuss how the interpolation of the curve between thresholds with binary predictors can largely change the AUC. Overall, we show using a linear interpolation from the ROC curve with binary predictors corresponds to the estimated AUC, which is most commonly done in software, which we believe can lead to misleading results. We compare R, Python, Stata, and SAS software implementations. We recommend using reporting the interpolation used and discuss the merit of using the step function interpolator, also referred to as the “pessimistic” approach by Fawcett (2006). Keywords ROC · AUC ·
Journal of Classification – Springer Journals
Published: Dec 23, 2019
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