In addition to accuracy, there are a number of other metrics used to evaluate classifiers. Two of the most common are precision and recall. To understand these two metrics, we must first understand false positives and false negatives. False positives happen when a classifier classifies a feature set with a label it shouldn't have gotten. False negatives happen when a classifier doesn't assign a label to a feature set that should have it. In a binary classifier, these errors happen at the same time.
Here's an example: the classifier classifies a movie review as
pos when it should have been
neg. This counts as a false positive for the
pos label, and a false negative for the
neg label. If the classifier ...