Simple guide to confusion matrix terminology

Positive Predictive Value: This is very similar to precision, except that it takes prevalence into account. In the case where the classes are perfectly balanced (meaning the prevalence is 50%), the positive predictive value (PPV) is equivalent to precision. This can be a useful baseline metric to compare your classifier against. However, the best classifier for a particular application will sometimes have a higher error rate than the null error rate, as demonstrated by the Accuracy Paradox. Cohen's Kappa: This is essentially a measure of how well the classifier performed as compared to how well it would have performed simply by chance.