FRM Part I · FRM Exam Part I · Machine-Learning Methods
A risk team evaluates a fraud classifier where only 1% of transactions are fraudulent. A model that labels every transaction as non-fraud achieves which result?
The model achieves 99% accuracy but 0% recall for fraud. It is right on all non-fraud cases, which make up 99% of the data, yet it catches no fraudulent transactions, illustrating why accuracy is a poor metric for imbalanced classes.
- AAccuracy of 99% but recall of 0% for fraudCorrect
- BAccuracy of 99% and recall of 99% for fraud
- CAccuracy of 50% and recall of 0% for fraud
- DAccuracy of 1% and recall of 100% for fraud
Explanation
Labeling everything as non-fraud is correct for 99% of cases, so accuracy is 99%. It detects no fraud, so TP = 0 and recall = 0. This shows accuracy misleads with imbalanced classes.
Did you get it right without looking?
One question tells you little. A timed set on Machine-Learning Methods shows your real accuracy, how long you take and where you lose marks.
More Machine-Learning Methods questions
- A neuron has two inputs x1 = 2 and x2 = -1, weights w1 = 0.5 and w2 = 1.5, and bias b = 0.25. The neuron uses a ReLU activation, f(z) = max(…
- A logistic regression for default has the form ln(p/(1-p)) = -3.0 + 0.5 x, where x is the debt-to-income ratio expressed in percentage point…
- A risk team chooses the penalty parameter lambda for an elastic net model used to predict loan defaults. Which procedure is most appropriate…
- During training of a deep neural network for credit scoring, the training error keeps falling while the validation error begins to rise afte…
- A risk team chooses the penalty parameter lambda for an Elastic Net model using k-fold cross-validation. Which description of the procedure …
- A bank has a dataset of 1,000 customers with a categorical feature 'region' that has four unordered categories: North, South, East and West.…