Howtosignalprocessing
- How do you compare two ROC curves?
- What is a ROC curve How can it be used to evaluate the performance of a classifier?
- Which performance measure will you consider for comparing different models based on ROC curve?
- When should ROC curve be used?
How do you compare two ROC curves?
If comparing 2 or more independent ROC curves, in the Y drop-down list, select the diagnostic test variable, and then in the Factor drop-down list, select the grouping variable. If comparing 2 or more paired/correlated ROC curves, in the Y list, select the diagnostic test variables. Click Calculate.
What is a ROC curve How can it be used to evaluate the performance of a classifier?
An ROC curve shows the relationship between clinical sensitivity and specificity for every possible cut-off. The ROC curve is a graph with: The x-axis showing 1 – specificity (= false positive fraction = FP/(FP+TN)) The y-axis showing sensitivity (= true positive fraction = TP/(TP+FN))
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.
When should ROC curve be used?
The ROC curve is used to assess the overall diagnostic performance of a test and to compare the performance of two or more diagnostic tests. It is also used to select an optimal cut-off value for determining the presence or absence of a disease.
