Loan quantity and interest due are two vectors through the dataset. </p> <p>One other three masks are binary flags (vectors) which use 0 and 1 to express if the certain conditions are met for the record that is certain. Mask (predict, settled) is made of the model prediction outcome: in the event that model predicts the mortgage to be settled, then your value is 1, otherwise, it’s 0. The mask is a purpose of limit since the forecast outcomes differ. On the other hand, Mask (real, settled) and Mask (true, past due) are a couple of reverse vectors: if the real label regarding the loan is settled, then value in Mask (true, settled) is 1, and vice versa.</p> <p>Then your income may be the dot item of three vectors: interest due, Mask (predict, settled), and Mask (real, settled). Expense may be the dot item of three vectors: loan quantity, Mask (predict, settled), and Mask (true, past due). The formulas that are mathematical be expressed below:</p> <p>Aided by the revenue thought as the essential difference between income and price, it really is calculated across all of the classification thresholds. The outcome are plotted below in Figure 8 for both the Random Forest model additionally the XGBoost model. The revenue is modified in line with the amount of loans, so its value represents the revenue to be manufactured per client.</p> <p><a href="" class="more-link post-excerpt-readmore">Read more</a></p> <p>