1
$\begingroup$

I have trained a classifier for 3 different classes, the test datasets of which are imbalanced, and then plotted the PR curves (below) to evaluate their accuracies. The plots contain the number of positives/negatives as well as the PR-AUC. The horizontal red line represents a baseline of the random classifier.

Similarly to the ROC that is evaluated with respect to the diagonal line, which would be produced by a completely random guess, I assume that I can also evaluate the PR curve with respect to the horizontal red line.

If this assumption is correct, can I say that the PR curves below indicate a good classification performance since they are all above the random guess line? PR-Curve of the first class PR-Curve of the first class PR-Curve of the first class

EDIT: figures have been updated.

$\endgroup$
3
$\begingroup$

"Good" is always subjective and problem-dependent: if the classification problem is easy, one would expect to beat the random classifier by a large margin, but for a difficult classification problem performing even just a bit better than random may be enough to be useful.

$\endgroup$
0
$\begingroup$

as @Franck Dernoncourt noted everything is relative. Your results just indicate that your models are better then "dumb" model.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.