Advantages of ROC curves

What is the advantages of the ROC curves?

For example I am classifying some images which is a binary classification problem.
I extracted about 500 features and applied a features selection algorithm to select a set of features then I applied SVM for classification. In this case how can I get a ROC curve?
Should I change the threshold values of my feature selection algorithm and get sensitivty and specificity of the output to draw a ROC curve?

In my case what is the purpose of creating a ROC curve?


Many binary classification algorithms compute a sort of classification score (sometimes but not always this is a probability of being in the target state), and they classify based upon whether or not the score is above a certain threshold. Viewing the ROC curve lets you see the tradeoff between sensitivity and specificity for all possible thresholds rather than just the one that was chosen by the modeling technique. Different classification objectives might make one point on the curve more suitable for one task and another more suitable for a different task, so looking at the ROC curve is a way to assess the model independent of the choice of a threshold.

Source : Link , Question Author : user570593 , Answer Author : Michael McGowan

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