3 ms·

Going by this graph[0], what they meant was: we had a precision of 93% and a recall of 90%. [0] http://web.stanford.edu/group/deepsolar/assets2/img/roc.jpg htt

• floatrock 8y ago
  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."