From: Martin C. Martin Hi, I have a bunch of data points x from two classes A & B, and I'm creating a classifier. So I have a function f(x) which estimates the probability that x is in class A. (I have an equal number of examples of each, so p(class) = 0.5.) One way of seeing how well this does is to compute the error rate on the test set, i.e. if f(x)>0.5 call it A, and see how many times I misclassify an item. That's what MASS does. But we should
Surely you mean `99% of dataminers/machine learners' rather than `MASS'?
be able to do better: misclassifying should be more of a problem if the regression is confident then if it isn't. How can I show that my f(x) = P(x is in class A) does better than chance?
It depends on what you mean by `better'. For some problem, people are perfectly happy with misclassifcation rate. For others, the estimated probabilities count a lot more. One possibility is to look at the ROC curve. Another possibility is to look at the calibration curve (see MASS the book). Andy
Thanks, Martin
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