FRM Part I · FRM Exam Part I · Machine-Learning Methods
A bank's credit model classifies 200 loan applicants as likely defaulters or non-defaulters. The confusion matrix shows 40 true positives (defaulters correctly flagged), 10 false positives, 20 false negatives and 130 true negatives. What is the model's precision for the default class?
Precision is 0.80. It is the share of loans flagged as defaults that actually defaulted: 40 true positives divided by 50 total flagged (40 true plus 10 false positives). The 0.67 figure is recall, which divides by actual defaulters instead of flagged loans.
- A0.80Correct
- B0.67
- C0.87
- D0.20
Explanation
Precision = TP/(TP+FP) = 40/(40+10) = 0.80. Recall would be 40/(40+20) = 0.67, which is the second option and uses false negatives in the denominator. Accuracy is 170/200 = 0.85.
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