Search results
Results From The WOW.Com Content Network
A receiver operating characteristic curve, or ROC curve, is a graphical plot that illustrates the performance of a binary classifier model (can be used for multi class classification as well) at varying threshold values. The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting.
Receiver Operating Characteristic Curve Explorer and Tester (ROCCET) is an open-access web server for performing biomarker analysis using ROC (Receiver Operating Characteristic) curve analyses on metabolomic data sets. [1] ROCCET is designed specifically for performing and assessing a standard binary classification test (disease vs. control).
The Partial Area Under the ROC Curve (pAUC) is a metric for the performance of binary classifier. It is computed based on the receiver operating characteristic (ROC) curve that illustrates the diagnostic ability of a given binary classifier system as its discrimination threshold is varied.
Youden's index is often used in conjunction with receiver operating characteristic (ROC) analysis. [4] The index is defined for all points of an ROC curve, and the maximum value of the index may be used as a criterion for selecting the optimum cut-off point when a diagnostic test gives a numeric rather than a dichotomous result.
The relationship between sensitivity and specificity, as well as the performance of the classifier, can be visualized and studied using the Receiver Operating Characteristic (ROC) curve. In theory, sensitivity and specificity are independent in the sense that it is possible to achieve 100% in both (such as in the red/blue ball example given above).
A receiver operating characteristic curve, or ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. The method was originally developed for operators of military radar receivers starting in 1941, which led to its name.
The receiver operating characteristic (ROC) also characterizes diagnostic ability, although ROC reveals less information than the TOC. For each threshold, ROC reveals two ratios, hits/(hits + misses) and false alarms/(false alarms + correct rejections), while TOC shows the total information in the contingency table for each threshold. [2]
Matched case-control analysis; Test for trend with count data; Independent t-test and one-way ANOVA; Diagnostic and screening test analyses with receiver operating characteristic (ROC) curves; Sample size for proportions, cross-sectional surveys, unmatched case-control, cohort, randomized controlled trials, and comparison of two means