Precision and Recall

One of the most important metrics used in evaluating the performance of binary classifiers is the Precision-Recall curve.

From Wikipedia:

It is possible to interpret precision and recall not as ratios but as probabilities:
* Precision is the probability that a (randomly selected) retrieved document is relevant.
* Recall is the probability that a (randomly selected) relevant document is retrieved in a search.

This page contains some illustrations to better understand the definitions of Precision and Recall:
http://newadonis.creighton.edu/hsl/searching/Recall-Precision.html

Also, this paper relates Precision-Recall curves to the Receiver Operator Characteristic curves:
http://www.biostat.wisc.edu/~page/rocpr.pdf

Finally, this page is further explaining the concepts and elaborating on the F-measure metric that tries to combine Precision and Recall into one measure:
http://streamhacker.com/2010/05/17/text-classification-sentiment-analysis-precision-recall/