To be able to balance the trade-off between your decline in revenue and a reduction in price, an optimization issue needs to be resolved by adjusting the limit and looking for the optimum.
Then by using the layout of the confusion matrix plotted in Figure 6, the four regions are divided as True Positive (TN), False Positive (FP), False Negative (FN) and True Negative (TN) if“Settled” is defined as positive and “Past Due” is defined as negative,. Aligned with all the confusion matrices plotted in Figure 5, TP may be the loans that are good, and FP may be the defaults missed. Our company is interested in both of these areas. To normalize the values, two widely used mathematical terms are defined: real good Rate (TPR) and False Positive Rate (FPR). Their equations are shown below:
In this application, TPR may be the hit price of good loans, plus it represents the ability of earning funds from loan interest; FPR is the rate that is missing of, also it represents the probability of taking a loss.
Receiver Operational Characteristic (ROC) bend is considered the most widely used plot to visualize the performance of the category model after all thresholds. In Figure 7 left, the ROC Curve for the Random Forest model is plotted. This plot really shows the partnership between TPR and FPR, where one always goes into the exact same way as one other, from 0 to at least one. a classification that is good would always have the ROC curve over the red standard, sitting because of the “random classifier”. The region Under Curve (AUC) can also be a metric for assessing the category model besides precision. The AUC regarding the Random Forest model is 0.82 away from 1, which can be decent.
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