Why is my ROC curve pointy?

Why is my ROC curve pointy?

A perfect ROC “curve” will be shaped with a sharp bend. The performance you have there is very near perfect separation. In addition, it looks like you have a scarcity of points making the curve.

What does ROC curve tells us?

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.

Why is my ROC curve 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.

What is a good ROC value?

AREA UNDER THE ROC CURVE In general, an AUC of 0.5 suggests no discrimination (i.e., ability to diagnose patients with and without the disease or condition based on the test), 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding.

What is a perfect ROC curve?

THE PERFECT TEST A perfect test is able to discriminate between the healthy and sick with 100 \% sensitivity and 100 \% specificity. It will have an ROC curve that passes through the upper left corner (~100 \% sensitivity and 100 \% specificity). The area under the ROC curve of the perfect test is 1.

What is the best ROC curve?

What is a good AUC ROC score?

Why is my ROC curve not smoothable (not enough points)?

Any such attempt will fail with the error “ROC curve not smoothable (not enough points).”. If the smooth ROC curve was generated by roc with density.controls and density.cases numeric arguments, it cannot be smoothed and the error “Cannot smooth a ROC curve generated directly with numeric ‘density.controls’ and ‘density.cases’.” is produced.

How many steps in a ROC curve?

$\\begingroup$A ROC curve is never smooth – the number of “steps” in a ROC curve depends on the number of thresholds you have available/use. It would seem that your analysis would use only three (or four judging by macro-average) thresholds (i don’t know the exact values of your python stuff) thresholds.

What does the area under the ROC curve represent?

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. In addition the area under the ROC curve gives an idea about the benefit of using the test(s)…

What is the significance of an ROC curve in Clinical Biochemistry?

ROC curves are used in clinical biochemistry to choose the most appropriate cut-off for a test. The best cut-off has the highest true positive rate together with the lowest false positive rate. As the area under an ROC curve is a measure of the usefulness of a test in general,…