Plotting Precision Recall Curve
In short, the precision-recall curve shows the trade-off between the two values as you change the strictness of the classifier.

There is a great explanation here, using the classification of images of airplanes and geese as an example.


  A good way to characterize the performance of a classifier is to look at how precision and recall change as you change the threshold. A good classifier will be good at ranking actual airplane images near the top of the list, and be able to retrieve a lot of airplane images before retrieving any geese: its precision will stay high as recall increases. A poor classifier will have to take a large hit in precision to get higher recall. Usually, a publication will present a precision-recall curve to show how this tradeoff looks for their classifier.