I have a classification problem where getting true positives is much more important than true negatives.

To be clear, I know that roughly 10% of my population are actual positives, but I can assign some proportion (say 30%) of the population to be classified as positives without much cost, above all else I need to make sure that the actual positives are covered by this set.

Unfortunately the classification tools I am using in WEKA seem to be balancing precision and recall, so that it is a) not assigning as many positives as it is allowed to, and b) getting quite a bad recall value.

Is there a standard way of approaching this problem? My first guess would be to weight the cost function towards recall rather than F-score, but I don't see an easy way to do this in WEKA.


1 Answer 1


Optimizing recall in model selection will likely yield trivial classifiers that label everything as positive (perfect recall), so that won't help.

I suggest plotting ROC curves or Precision-Recall curves for your existing models to determine a decision threshold with the recall you desire. After that you can compare which models have the best specificity or precision (direct result of ROC and PR curves, respectively).

  • $\begingroup$ Yeah I thought about something like this. Unfortunately I think I need to do some optimisation before the ROC stage - at the moment there are a lot of positives getting a very low score. $\endgroup$ Feb 27, 2014 at 11:00
  • $\begingroup$ I agree that weighting 100% recall is a bad idea, but there are compromises, like the F_{beta} score - en.wikipedia.org/wiki/F1_score $\endgroup$ Feb 27, 2014 at 11:01
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    $\begingroup$ Try weighting positives (or take multiple samples of them) to force a classifier to put more emphasis on positives. $\endgroup$ Feb 27, 2014 at 11:09
  • $\begingroup$ @sweezyjeezy However, the F1 score does not give higher weights towards Recall. It just assures that preference is given to procedures that prevent 0% recall and 100% precision to have a mean of 50%, compared to 50%-50%, which is often much preferred. $\endgroup$ Feb 27, 2014 at 11:09
  • $\begingroup$ Taking multiple samples is a good idea, I will try this $\endgroup$ Feb 27, 2014 at 11:21

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