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Consider an input matrix $X$ and a binary output $y$.

A common way to measure the performance of a classifier is to use ROC curves.

In a ROC plot the diagonal is the result that would be obtained from a random classifier. In case of an unbalanced output $y$ the performance of a random classifier can be improved choosing $0$ or $1$ with different probabilities.

How can the performance of such classifier be represented in a ROC curve plot? I suppose it should be a straight line with a different angle, and not the diagonal anymore?

ROC curve example

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ROC curves are insensitive to class balance. The straight line you obtain for a random classifier now is already the result of using different probabilities of yielding positive (0 brings you to (0, 0) and 1 brings you to (1, 1) with any range inbetween).

Nothing changes in an imbalanced setting.

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    $\begingroup$ I find it helpful to consider the meaning of the area under curve to see why the diagonal does not change. AUC can be interpreted as the probability that a randomly selected positive example will have a higher score than a randomly selected negative example.1. This makes it clearer to me why class imbalance is not an issue. $\endgroup$ – JBecker Jan 7 '15 at 22:01

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