I want to verify that the logic of the way I am producing ROC curves is correct. (irrelevant of the technical understanding of the actual code). I have a data set which I want to classify. I am using a neural network specifically
MLPClassifier function form python's
scikit Learn module.
I am passing a training data set to the
fit function and then using the
predict function with the testing data set. I am then outputting a confusion matrix with a false positive value and a true positive value.
what I would like to do is calculate a ROC curve where I need a set of true positive and false positive values. would it make sense to run the neural network (
MLPClassifier) multiple times with different targets each time and record the different true positive and false positive values?
It seems simple enough for me. Is it the right way to produce a ROC curve with a neural network?