Analysis of ROC and Precision-Recall curve
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I have run some machine learning experiments and now I have created some ROC and Precision-Recall curves (with the help of a toolbox).
Unfortunately, I'm not familiar with these two things. Of course, in the web there is plenty of material describing it but I did not find some good explanation based on an example.
Is there somebody who can show how one can analyse classifiers and also compare them based on the ROC and Precision-Recall curve? Perhaps this could easier be understood when following an example.
I would highly appreciate if somebody can bring light into the dark.