Machine Learning Has Transformed Many Aspects Of Everyday Life

For example, it is important to understand how the business will use the model's results. Typically, scores are combined with a single threshold to convert it into a decision procedure (i.e.: fast track applications with scores lower than certain level, assumed to be low risk). To do this, a balance between the true-positives (applications the model correctly classifies as high risk), false-positives (applications the model scores as high risk but are not) and the false-negatives (applications the model scores as low risk but were in fact high risk) is essential. I suggest using ROC curves, including the AUC (area under the curve) as a proxy measure for tuning scoring procedures until a good trade-off is found.