 For a binary classification problem involving N examples, each prediction will fall into one of these four categories. The performance of your classifier with respect to those N examples can then be assessed. A perfectly inaccurate classifier will have all zeros in the diagonal; a perfectly accurate classifier will have all zeros in the counterdiagonal. The precision of your classifier is the ratio of true positives to all positive predictions, that is true positives / (true positives + false positives). The recall of your classifier is the ratio of true positives to all true predictions, that is true positives / (true positives + false negatives).