tp tp+fp precision is used as performance metric when the goal is to limit the number of false positives as an exampleimagine model for predicting whether new drug will be effective in treating disease in clinical trials clinical trials are notoriously expensiveand pharmaceutical company will only want to run an experiment if it is very sure that the drug will actually work thereforeit is important that the model does not produce many false positives--in other wordsthat it has high precision precision is also known as positive predictive value (ppvrecallon the other handmeasures how many of the positive samples are captured by the positive predictionsrecall tp tp+fn recall is used as performance metric when we need to identify all positive samplesthat iswhen it is important to avoid false negatives the cancer diagnosis example from earlier in this is good example for thisit is important to find all people that are sickpossibly including healthy patients in the prediction other names for recall are sensitivityhit rateor true positive rate (tprthere is trade-off between optimizing recall and optimizing precision you can trivially obtain perfect recall if you predict all samples to belong to the positive class-there will be no false negativesand no true negatives either howeverpredicting all samples as positive will result in many false positivesand therefore the precision will be very low on the other handif you find model that predicts only the single data point it is most sure about as positive and the rest as negativethen precision will be perfect (assuming this data point is in fact positive)but recall will be very bad precision and recall are only two of many classification measures derived from tpfptnand fn you can find great summary of all the measures on wikipedia in the machine learning communityprecision and recall are arguably the most commonly used measures for binary classificationbut other communities might use other related metrics sowhile precision and recall are very important measureslooking at only one of them will not provide you with the full picture one way to summarize them is the -score or -measurewhich is with the harmonic mean of precision and recallf= precision*recall precision+recall evaluation metrics and scoring