Wikipedia has a good illustration showing the relation between precision & recall vs. false positives & false negatives: https://en.wikipedia.org/wiki/Precisio

Wikipedia has a good illustration showing the relation between precision & recall vs. false positives & false negatives: https://en.wikipedia.org/wiki/Precision_and_recall https://en.wikipedia.org/wiki/Precision_and_recall Precision is what percent of the identified-positives are actually positives: true-positives / (true-positives + false-positives). Recall is what percent of the actual-positives were identified as positive: true-positives / (true-positives + false-negatives). These are useful summary stats over the true-false/positive-negative matrix because, to quote the wikipedia: "In simple terms, high precision means that an algorithm returned substantially more relevant results than irrelevant ones, while high recall means that an algorithm returned most of the relevant results."