ROC Curve

MATLAB function which performs a ROC curve of two-class data.
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Aggiornato 13 dic 2018

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This function calculates the Receiver Operating Characteristic curve, which represents the 1-specificity and sensitivity of two classes of data, (i.e., class_1 and class_2).

The function also returns all the needed quantitative parameters: threshold position, distance to the optimum point, sensitivity, specificity, accuracy, area under curve (AROC), positive and negative predicted values (PPV, NPV), false negative and positive rates (FNR, FPR), false discovery rate (FDR), false omission rate (FOR), F1 score, Matthews correlation coefficient (MCC), Informedness (BM) and Markedness; as well as the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN).

Example of use:
class_1 = 0.5*randn(100,1);
class_2 = 0.5+0.5*randn(100,1);
roc_curve(class_1, class_2);

Cita come

Víctor Martínez-Cagigal (2025). ROC Curve (https://www.mathworks.com/matlabcentral/fileexchange/52442-roc-curve), MATLAB Central File Exchange. Recuperato .

Compatibilità della release di MATLAB
Creato con R2014a
Compatibile con qualsiasi release
Compatibilità della piattaforma
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Categorie
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Versione Pubblicato Note della release
3.1

All parameters are now printed in the CMD.

3.0

The function now outputs more parameters.

2.1.0.0

Classes are now indicated separately.

2.0.0.0

Different sizes in class_1 and class_2 are now allowed.

1.1.0.0

Fixed a bug in the output data.

1.0.0.0