FRM Part I · FRM Exam Part I · Machine-Learning Methods
A default classifier is tested on 200 loans. Results: 30 true positives, 10 false positives, 20 false negatives, and 140 true negatives. What are the model's precision and recall for the default class?
Precision is 0.750 and recall is 0.600. Precision is true positives over all predicted defaults, 30/(30+10), while recall is true positives over all actual defaults, 30/(30+20). The two are different denominators and should not be swapped.
- APrecision 0.750; recall 0.600Correct
- BPrecision 0.600; recall 0.750
- CPrecision 0.750; recall 0.900
- DPrecision 0.857; recall 0.600
Explanation
Precision = TP/(TP+FP) = 30/40 = 0.75. Recall = TP/(TP+FN) = 30/50 = 0.60. Swapping the two gives option 2; 0.857 is specificity-like (140/150 is 0.933) or other misuse and is incorrect.
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 hidden node in a neural network receives three inputs x1 = 2, x2 = -1 and x3 = 3 with weights 0.5, 2.0 and -0.4 respectively, and a bias o…
- A risk manager lowers the probability threshold at which a logistic model classifies a borrower as a defaulter, from 0.50 to 0.30. Which out…
- An analyst standardizes a feature using the training sample mean of 12 and a training standard deviation of 4. A new observation in the test…
- A trading desk builds an algorithm that repeatedly chooses how much of a position to execute each minute. After each action it observes the …
- A risk analyst builds a feedforward neural network to predict loan default. Which statement best describes the role of the activation functi…
- A risk analyst fits a single classification tree to predict loan default and lets it grow until every training observation sits in a pure le…