# Did I understand ROC curves correctly?

I want to classify objects by their area into two classes. I implemented several area estimates that I want to compare. For each object, I have a gold standard indicating to which of the two classes each object belongs.

The area estimators give me a binary output, telling to which of the two classes the object belongs taking a threshold.

Now I want to compare these binary classifiers where each of them also applies several thresholds for the classification. So for every threshold, I can calculate the true positive rate and false positive rate and plot these values against each other. So I get one curve for each classifier and every datapoint stands for one threshold that is applied.

Reading through articles about ROC plots my impression is that in most cases they are used in a different context, namely the classifier outputs are not binary values but probablities to which a threshold is applied.

Are my TPR vs. FPR plots even ROC plots? Does my approach even make sense?

edit: the classifiers I want to compare and for which I want to compare thresholds estimate areas: area = [ 11.2, 20.3, 1.2, 3 ]; and apply a threshold to this area, resulting in the binary outputs outputs = [ 0, 0, 1, 1];

• can you give a couple of rows of your training data (with input and output variables) to clarify. – Zhubarb Jun 19 '15 at 7:34

• A common example of non-probabilistic scores is the (signed) distance to the separating hyperplane for SVMs, for which scores are in $\mathbb{R}$. – Marc Claesen Jun 19 '15 at 9:14