Howtosignalprocessing
- How do you know which ROC curve is better?
- What are the limitations of ROC curves?
- How are ROC curves generated?
- Which performance measure will you consider for comparing different models based on ROC curve?
How do you know which ROC curve is better?
COMPARING ROC CURVES
The closer an ROC curve is to the upper left corner, the more efficient is the test. In FIG. XIII test A is superior to test B because at all cut-offs the true positive rate is higher and the false positive rate is lower than for test B.
What are the limitations of ROC curves?
Confidence scores used to build ROC curves may be difficult to assign. False-positive and false-negative diagnoses have different misclassification costs. Excessive ROC curve extrapolation is undesirable. Net benefit methods may provide more meaningful and clinically interpretable results than ROC AUC.
How are ROC curves generated?
ROC curves are graphic representations of the relation existing between the sensibility and the specificity of a test. It is generated by plotting the fraction of true positives out of the total actual positives versus the fraction of false positives out of the total actual negatives.
Which performance measure will you consider for comparing different models based on ROC curve?
The Area Under the Curve (AUC) is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve. The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes.
