While I was reading Torgo's Data Mining with R, I found that the description of precision/recall curve was different compared with other approaches. Usually, these curves are based on a threshold that determines which probability value is good enough to decide when an event has occurred, so we can classify future events depending on that value. However, Torgo's description is as follows:

Precision/recall (PR) curves are visual representations of the performance of a model in terms of the precision and recall statistics. The curves are ob- tained by proper interpolation of the values of the statistics at different working points. These working points can be given by different cut-off limits on a ranking of the class of interest provided by the model. In our case this would correspond to different effort limits applied to the outlier ranking produced by the models. Iterating over different limits (i.e., inspect less or more reports), we get different values of precision and recall. PR curves allow this type of analysis.

The application the author has in mind is that of a fraud detection problem in which we have a classification task resulting in values fraud, unknown and ok. We would like to output probabilities, rank them, select the first $k$ reports and be able to inspect them.

Is this an alternative measure of threshold in precision/recall curves? I think it is assuming that probabilities below 0.5 are to be classified as ok, 0.5 is equivalent to unknown and above 0.5 means fraud. Is that a correct assumption to make?

Thanks a lot!


2 Answers 2


Short answer: Torgo describes the usual method of generating such curves.

You can choose your threshold (= cut-off limit in the cited text) at any value. The cited text refers to one such choice as a working point.
That is, for a given working point, you'll observe exactly one (precision; recall) pair, i.e. one point in your graph. The precision-recall-curve is obtained by varying the threshold over the whole range of the classifier's continuous output ("scores", posterior probabilities, "votes") thus generating a curve from many working points.

Edit with respect to the comment:

I think "varying the threshold" is the usual way to explain or define the curve.

For the calculation, it is more efficient to sort the scores, and then see how precision and recall change when adding the next case: precision and recall can only change when the change in the threshold is large enough to cover the next score.

Consider this example:

case   true class   predicted score (high => class B)
1      A            0.2
3      B            0.5
2      A            0.6
4      B            0.9

threshold      recall    precision
> 0.9          N/A       0.0
(0.6, 0.9]     0.5       1.0        
(0.5, 0.6]     0.5       0.5
(0.2, 0.5]     1.0       0.67
< 0.2          1.0       0.5

That is, the precision-recall-curve acutally consists of points. It jumps from one point to the next when the threshold "crosses" an acutally predicted score. A smooth curve will result only for large numbers of test cases.

  • $\begingroup$ Thank you for your answer. Is this the usual method? Other machine learning books describe what you say. Maybe it is not entirely clear from the quote above, but the cut-off limits that Torgo is using refers to the number of instances that are inspected, which is quite different from the classifier's output. $\endgroup$
    – r_31415
    Commented Dec 27, 2013 at 19:25
  • 1
    $\begingroup$ @RobertSmith the proportion of instances that are inspected is directly related to the classifier's output because instances are ranked based on said output. Every proportion to be inspected corresponds to a certain threshold on the classifier's decision value. $\endgroup$ Commented Mar 11, 2014 at 9:40

For your aim, precision/recall curves are not that relevant. Yours is a probability estimation problem, something that logistic regression and its many variations can do quite nicely. There is nothing magic about a probability threshold of 0.5 nor is it needed. You can choose $k$ based on cost and inspect the units with the $k$ highest predicted probabilities. Also, plot a lift curve.

The estimated probabilities are also self-contained error rates. Always keep then handy during the inspection process as this will make the inspectors aware of "close calls."


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