patient will undergo additional screening herewe would call positive test (an indication of cancerthe positive classand negative test the negative class we can' assume that our model will always work perfectlyand it will make mistakes for any applicationwe need to ask ourselves what the consequences of these mistakes might be in the real world one possible mistake is that healthy patient will be classified as positiveleading to additional testing this leads to some costs and an inconvenience for the patient (and possibly some mental distressan incorrect positive prediction is called false positive the other possible mistake is that sick patient will be classified as negativeand will not receive further tests and treatment the undiagnosed cancer might lead to serious health issuesand could even be fatal mistake of this kind--an incorrect negative prediction--is called false negative in statisticsa false positive is also known as type errorand false negative as type ii error we will stick to "false negativeand "false positive,as they are more explicit and easier to remember in the cancer diagnosis exampleit is clear that we want to avoid false negatives as much as possiblewhile false positives can be viewed as more of minor nuisance while this is particularly drastic examplethe consequence of false positives and false negatives are rarely the same in commercial applicationsit might be possible to assign dollar values to both kinds of mistakeswhich would allow measuring the error of particular prediction in dollarsinstead of accuracy this might be much more meaningful for making business decisions on which model to use imbalanced datasets types of errors play an important role when one of two classes is much more frequent than the other one this is very common in practicea good example is click-through predictionwhere each data point represents an "impression,an item that was shown to user this item might be an ador related storyor related person to follow on social media site the goal is to predict whetherif shown particular itema user will click on it (indicating they are interestedmost things users are shown on the internet (in particularadswill not result in click you might need to show user ads or articles before they find something interesting enough to click on this results in dataset where for each "no clickdata pointsthere is "clickeddata pointin other words of the samples belong to the "no clickclass datasets in which one class is much more frequent than the other are often called imbalanced datasetsor datasets with imbalanced classes in realityimbalanced data is the normand it is rare that the events of interest have equal or even similar frequency in the data now let' say you build classifier that is accurate on the click prediction task what does that tell you accuracy sounds impressivebut this doesn' take the evaluation metrics and scoring