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ROC curve

versión 2.0.0.0 (17.9 KB) por Giuseppe Cardillo
compute a ROC curve
4.4
(34)

41,6K descargas

Actualizada 1 Sep 2021

De GitHub

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ROC - Receiver Operating Characteristics.
The ROC graphs are a useful technique for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making.
YOU CAN USE THIS FUNCTION ONLY AND ONLY IF YOU HAVE A BINARY CLASSIFICATOR.
The input is a Nx2 matrix: in the first column you will put your test values (i.e. glucose blood level); in the second column you will put only 1 or 0 (i.e. 1 if the subject is diabetic; 0 if he/she is healthy).
Run rocdemo to see an example

The function computes and plots the classical ROC curve and curves for Sensitivity, Specificity and Efficiency (see the screenshot).

The function will show 6 cut-off points:
1) Max sensitivity
2) Max specificity
3) Cost effective (Sensitivity=Specificity)
4) Max Efficiency
5) Max PLR
6) Max NLR

ROC requires the Curve fitting toolbox.

Citar como

Giuseppe Cardillo (2022). ROC curve (https://github.com/dnafinder/roc), GitHub. Recuperado .

Compatibilidad con la versión de MATLAB
Se creó con R2014b
Compatible con cualquier versión
Compatibilidad con las plataformas
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Para consultar o informar de algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.
Para consultar o informar de algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.