decreasing the number of hospital admissions it could also be getting more users for your websiteor having users spend more money in your shop when choosing model or adjusting parametersyou should pick the model or parameter values that have the most positive influence on the business metric often this is hardas assessing the business impact of particular model might require putting it in production in real-life system in the early stages of developmentand for adjusting parametersit is often infeasible to put models into production just for testing purposesbecause of the high business or personal risks that can be involved imagine evaluating the pedestrian avoidance capabilities of self-driving car by just letting it drive aroundwithout verifying it firstif your model is badpedestrians will be in troubletherefore we often need to find some surrogate evaluation procedureusing an evaluation metric that is easier to compute for examplewe could test classifying images of pedestrians against nonpedestrians and measure accuracy keep in mind that this is only surrogateand it pays off to find the closest metric to the original business goal that is feasible to evaluate this closest metric should be used whenever possible for model evaluation and selection the result of this evaluation might not be single number--the consequence of your algorithm could be that you have more customersbut each customer will spend less--but it should capture the expected business impact of choosing one model over another in this sectionwe will first discuss metrics for the important special case of binary classificationthen turn to multiclass classification and finally regression metrics for binary classification binary classification is arguably the most common and conceptually simple application of machine learning in practice howeverthere are still number of caveats in evaluating even this simple task before we dive into alternative metricslet' have look at the ways in which measuring accuracy might be misleading remember that for binary classificationwe often speak of positive class and negative classwith the understanding that the positive class is the one we are looking for kinds of errors oftenaccuracy is not good measure of predictive performanceas the number of mistakes we make does not contain all the information we are interested in imagine an application to screen for the early detection of cancer using an automated test if we ask scientifically minded readers to excuse the commercial language in this section not losing track of the end goal is equally important in sciencethough the authors are not aware of similar phrase to "business impactbeing used in that realm model evaluation and improvement