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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.
[2] [3] [4] It has an integrated spreadsheet for data input and can import files in several formats (Excel, SPSS, CSV, ...). MedCalc includes basic parametric and non-parametric statistical procedures and graphs such as descriptive statistics , ANOVA , Mann–Whitney test , Wilcoxon test , χ 2 test , correlation , linear as well as non-linear ...
An image of different ROC curves is shown in Figure 1. ROC curves provide a simple visual method for one to determine the boundary limit (or the separation threshold) of a biomarker or a combination of biomarkers for the optimal combination of sensitivity and specificity. The AUC (area under the curve) of the ROC curve reflects the overall ...
The TOC curve with four boxes indicates how a point on the TOC curve reveals the hits, misses, false alarms, and correct rejections. The TOC curve is an effective way to show the total information in the contingency table for all thresholds. The data used to create this TOC curve is available for download here. This dataset has 30 observations ...
Youden's index is often used in conjunction with receiver operating characteristic (ROC) analysis. [3] 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.
An example of ROC curve and the area under the curve (AUC). The area under the ROC curve (AUC) [1] [2] is often used to summarize in a single number the diagnostic ability of the classifier. 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 regions where the values of FPR ...
The output is called a CAP curve. [1] The CAP is distinct from the receiver operating characteristic (ROC) curve, which plots the true-positive rate against the false-positive rate. CAPs are used in robustness evaluations of classification models.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]