How to interpret FPR and TPR in ROC curve? I am playing with ROC and trying to draw some curves. I am using example from this scikit page. I do not understand one thing: when I print out the content of tpr and fpr, I see two arrays of numbers (21 elems in each) - why is that?

TPR (True Positive Ratio) is a proportion of those tuples classified as positives to all real positive tuples. So, it should be one number

I see it as follow: I take classifier (like Decision Tree), train it on some data and finally test it. Then I can calculate TPR and FPR and I should have only two values.

Instead, I receive arrays. Why is that? I tried to change classifier from that example to Decision Tree and then I receive only one point (which is usually [0, 1]). How can I draw ROC curve on decision tree? Or - to be more precise with my question - why do I get an array of values as my TRP/FPR instead of single values?
As explained here in the docs of the function returning the ROC Curve 
they return a array because when you interpret a ROC curve what you're looking the performance of the predictor given a threshold. So, it means that, if I'm looking for only classifications that the model is above 80% sure, the ROC curve will have a different TPR/FPR than a threshold of 90%.

If you have further questions, a great link explaining it: http://www.dataschool.io/roc-curves-and-auc-explained/