Is ROC curve concave or convex?

Is ROC curve concave or convex?

From this definition, ROC curves that contain straight line segments are considered to be convex.

What should an ROC curve look like?

The ROC curve shows the trade-off between sensitivity (or TPR) and specificity (1 – FPR). Classifiers that give curves closer to the top-left corner indicate a better performance. The closer the curve comes to the 45-degree diagonal of the ROC space, the less accurate the test.

What if ROC curve is a straight line?

A ROC curve of a random classifier A classifier with the random performance level always shows a straight line from the origin (0.0, 0.0) to the top right corner (1.0, 1.0). Two areas separated by this ROC curve indicates a simple estimation of the performance level.

How do you interpret the ROC curve?

Interpreting the ROC curve. The ROC curve shows the trade-off between sensitivity (or TPR) and specificity (1 – FPR). Classifiers that give curves closer to the top-left corner indicate a better performance. As a baseline, a random classifier is expected to give points lying along the diagonal (FPR = TPR).

What is the use of a redroc curve?

ROC curves are frequently used to show in a graphical way the connection/trade-off between clinical sensitivity and specificity for every possible cut-off for a test or a combination of tests.

What is the ROC curve of a random classifier?

Interpreting the ROC curve The ROC curve shows the trade-off between sensitivity (or TPR) and specificity (1 – FPR). Classifiers that give curves closer to the top-left corner indicate a better performance. As a baseline, a random classifier is expected to give points lying along the diagonal (FPR = TPR).

What is the area under the ROC curve of the worthless test?

The ROC curve of the worthless test falls on the diagonal line. It includes the point with 50 \% sensitivity and 50 \% specificity. The area under the ROC curve of the worthless test is 0.5. FIG.