Partial Area Under the ROC Curve
... the ROC curve of C a {\displaystyle C_{a}} is never above the ROC curve of C b {\displaystyle C_{b}} the ROC curve of C a {\ ... The AUC is simply defined as the area of the ROC space that lies below the ROC curve. However, in the ROC space there are ... displaystyle C_{a}} is never below the ROC curve of C b {\displaystyle C_{b}} the classifiers' ROC curves cross each other. ... Thus, the partial AUC was computed as the area under the ROC curve in the vertical band of the ROC space where FPR is in the ...
MedCalc
Procedures for method evaluation and method comparison include ROC curve analysis, Bland-Altman plot, as well as Deming and ... ISBN 978-0-4298-7787-2. Krzanowski, Wojtek J.; Hand, David J. (2009). ROC Curves for Continuous Data. Boca Raton, FL: Chapman ...
Receiver Operating Characteristic Curve Explorer and Tester
As a rule of thumb, the fewer the biomarkers that one uses to maximize the AUC of the ROC curve, the better. ROCCET's ROC curve ... An image of different ROC curves is shown in Figure 1. ROC curves provide a simple visual method for one to determine the ... ROC) curve. ROC curves plot the sensitivity of a biomarker on the y axis, against the false discovery rate (1- specificity) on ... The AUC (area under the curve) of the ROC curve reflects the overall accuracy and the separation performance of the biomarker ( ...
Evaluation of binary classifiers
Another useful single measure is "area under the ROC curve", AUC. An F-score is a combination of the precision and the recall, ... ROC) curve. In theory, sensitivity and specificity are independent in the sense that it is possible to achieve 100% in both ( ... Chicco D.; Jurman G. (2023). "The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for ... Powers, David M. W. (2011). "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation". ...
Rattle GUI
Evaluation = Confusion Matrix, Risk Charts, Cost Curve, Hand, Lift, ROC, Precision, Sensitivity. Charts = Box Plot, Histogram, ...
Diagnostic odds ratio
The value a can be used to plot a summary ROC (SROC) curve. Consider a test with the following 2×2 confusion matrix: We ... Moses, L. E.; Shapiro, D; Littenberg, B (1993). "Combining independent studies of a diagnostic test into a summary ROC curve: ...
Biometrics
The value of the EER can be easily obtained from the ROC curve. The EER is a quick way to compare the accuracy of devices with ... Receiver operating characteristic or relative operating characteristic (ROC): The ROC plot is a visual characterization of the ... different ROC curves. In general, the device with the lowest EER is the most accurate. Failure to enroll rate (FTE or FER): the ...
Predictive modelling
It achieved an area under the ROC (Receiver Operating Characteristic) curve of 0.89. To provide explain-ability, they developed ...
David Hand (statistician)
... a coherent alternative to the area under the ROC curve. Machine Learning, 77, 103-123 Hand D.J. (2018) Statistical challenges ... A coherent alternative to the area under the ROC curve". Machine Learning. 77: 103-123. doi:10.1007/s10994-009-5119-5. S2CID ...
List of RNA-Seq bioinformatics tools
Includes a tool for grading and generating ROC curves from resultant sam files. Open-source, written in pure Java; supports all ...
Total operating characteristic
It is common to report the area under the curve (AUC) to summarize a TOC or ROC curve. However, condensing diagnostic ability ... of the AUC is consistent for the same data whether you are calculating the area under the curve for a TOC curve or a ROC curve ... The curve shows accurate diagnosis of presence until the curve reaches a threshold of 86. The curve then levels off and ... At any given point in the ROC curve, it is possible to glean values for the ratios of false alarms/(false alarms+correct ...
Detection theory
... such as the area under the ROC-curve. Bias is the extent to which one response is more probable than another, averaging across ...
Data mining
Several statistical methods may be used to evaluate the algorithm, such as ROC curves. If the learned patterns do not meet the ...
Cross-validation (statistics)
"An experimental comparison of cross-validation techniques for estimating the area under the ROC curve". Computational ... as leave-pair-out cross-validation has been recommended as a nearly unbiased method for estimating the area under ROC curve of ...
Mann-Whitney U test
This is the same as the area under the curve (AUC) for the ROC curve. A statistic called ρ that is linearly related to U and ... Hand, David J.; Till, Robert J. (2001). "A Simple Generalisation of the Area Under the ROC Curve for Multiple Class ... The U statistic is related to the area under the receiver operating characteristic curve[citation needed] (AUC). A U C 1 = U 1 ... Boston University (SPH), 2017 Fawcett, Tom (2006); An introduction to ROC analysis, Pattern Recognition Letters, 27, 861-874. ...
Docking (molecular)
The area under the receiver operating characteristic (ROC) curve is widely used to evaluate its performance. Resulting hits ...
Gini coefficient
ROC) curve and its diagonal. It is related to the AUC (Area Under the ROC Curve) measure of performance given by A U C = ( G + ... states by Gini coefficient Lorenz curve Matthew effect Pareto distribution ROC analysis Suits index The Elephant Curve Utopia ... Hand, David J.; Till, Robert J. (2001). "A Simple Generalisation of the Area Under the ROC Curve for Multiple Class ... The Gini coefficient is usually defined mathematically based on the Lorenz curve, which plots the proportion of the total ...
List of datasets for machine-learning research
Bradley, Andrew P (1997). "The use of the area under the ROC curve in the evaluation of machine learning algorithms" (PDF). ... "The Learning Curve Method Applied to Clustering." AISTATS. 2001. Fanaee-T, Hadi; Gama, Joao (2013). "Event labeling combining ... Kudo, Mineichi; Toyama, Jun; Shimbo, Masaru (1999). "Multidimensional curve classification using passing-through regions". ...
Gene expression programming
More exotic fitness functions that explore model granularity include the area under the ROC curve and rank measure. Also ...
Receiver operating characteristic
the area between the full ROC curve and the triangular ROC curve including only (0,0), (1,1) and one selected operating point ... Sometimes, the ROC is used to generate a summary statistic. Common versions are: the intercept of the ROC curve with the line ... Under these assumptions, the shape of the ROC is entirely determined by d′. However, any attempt to summarize the ROC curve ... Several studies criticize the usage of the ROC curve and its area under the curve as measurements for assessing binary ...
Net reclassification improvement
Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med ... This is not the case for other metrics such as area-under-the-curve, Brier score or net benefit. PredictABEL: an R package for ... Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation. 2007;115(7):928-935. Pencina MJ ...
Computational Resource for Drug Discovery
... area under curve and precision/recall curve. The parametrization can be visualized by coloring the curve according to cutoff. ... ROCR: The ROCR is an R package for evaluating and visualizing classifier performance . It is a flexible tool for creating ROC ... It includes a function, AUC, to calculate area under the curve. It also includes functions for half-life estimation for a ... between the dosing regimen and the body's exposure to the drug as measured by the nonlinear concentration time curve. ...
Demining
One quantitative measure is a receiver operating characteristic (ROC) curve, which measures the tradeoff between false ... Ideally, there should be a high probability of detection with few false positives, but such curves have not been obtained for ...
Cumulative accuracy profile
The output is called a CAP curve. The CAP is distinct from the receiver operating characteristic (ROC) curve, which plots the ... The cumulative accuracy profile (CAP) and ROC curve are both commonly used by banks and regulators to analyze the ... and a randomized curve. A good model will have a CAP between the perfect and random curves; the closer a model is to the ... A cumulative accuracy profile can be used to evaluate a model by comparing the current curve to both the 'perfect' ...
Evidence-based medicine
AUC-ROC The area under the receiver operating characteristic curve (AUC-ROC) reflects the relationship between sensitivity and ... Cutoff values for positive and negative tests can influence specificity and sensitivity, but they do not affect AUC-ROC. Number ... High-quality tests will have an AUC-ROC approaching 1, and high-quality publications about clinical tests will provide ...
Materials MASINT
ROC curves are commonly drawn to show sensitivity as a function of false positive rate for a given detection confidence and ... ROC). These parameters are sensitivity, probability of correct detection, false positive rate and response time. Ideally, the ...
Statistical classification
More recently, receiver operating characteristic (ROC) curves have been used to evaluate the tradeoff between true- and false- ...
Polygenic score
... is the area under the ROC curve (AUC). Some example results of PGS performance, as measured in AUC (0 ≤ AUC ≤ 1 where a larger ...
Machine learning in bioinformatics
Based on analysis of the ROC curves, a suitable score cutoff was chosen for the prediction of cleavage sites in lanthipeptides ...
Burst suppression
When the features represent distinguishable patterns of burst and suppression, a fixed threshold using ROC-curve or machine ...