Optimising for Precision-Recall curves under class imbalance
I built a classification model and tested it against a validation data set. The positive set is composed of 86 cases and the negative set is composed of 1256 cases. The confusion matrix is as follows
True positive True negative precision Predict positive 55 338 13.99% Predict negative 31 918 96.73% Recall 63.95% 73.09%
The precision and recall for this classifier is not good, especially for the positive precision. However, the negative cases are much more than the positive case. I am not quite sure that, for this kind of unbalanced data, can we still use the precision and recall as the performance evaluation as usual?