Consider a situation where we are running a classifier (the actual classification algorithm doesn't matter here), and the class labels are given based on a score. If score > 0, the data point is labeled A, if score < 0, the data point is labeled B.
All the training data contains data points with positive or negative scores. However, in my test data, there are a few points that return score = 0. How should I measure the precision and recall in this scenario where some points can't be classified into any class?
[additional information from a comment below] I faced this in a sentiment classification task. The normalized scores are in the range [-1,1], with 0 being the score for documents with no sentiment. It so happened that I had no neutral documents in my training data, but in the test data, some documents returned a score of 0.