How to use ROC curve

1
votes

For Logistic regression we usually follow below approach ---

[1] Randomly initiallize parameters (theta), and choose cutoff/deciding point (we consider points above this cutoff point as one class and below ones as another class)

[2] Predict output values (h) with theta and chosen input features

[3] Calculate cost using predicted (h) and actual result

[4] Calculate gradient, so that we can minimize theta using it

[5] Recalculate theta using obtained gradient

[6] repeat steps 1-5 for few iterations and then plot the cost values (obtained in 3rd step of each iteration) against no of iteration

[7] If the cost values are getting decreased with increase in no of iterations, then our classifier is good otherwise we have to randomly choose another value of theta and start against

We use ROC curve to analyse the trade off between cutoff point and true positive as well as true negative rate. My question is when can we use ROC curve? Is it after finding the minimized theta using gradient descent? Please help!!

1
machine-learning

1 Answers

1
votes

The ROC curve can be measured for any tunable predictor, no matter how bad. You could measure it straight away after step one. That would get you a very bad curve, of course: simultaneously many FP and FN.

The whole point of all those iterations is to push the ROC curve lower.