ISACA Certified Artificial Intelligence Auditor (CAIA) — Question 16

A model developer is looking to mitigate the risk of information loss within an imbalanced data set that contains majority and minority classes. Which of the following strategies would an IS auditor consider to be MOST appropriate?

Answer options

• A. Undersampling to remove excess data points
• B. Splitting data into к-folds with cross-validation
• C. Tuning the model’s hyperparameters
• D. Oversampling to create new data points

Correct answer: D

Explanation

The correct answer is D, as oversampling helps to balance the classes by creating additional instances of the minority class, thus mitigating information loss. Option A, undersampling, may lead to loss of valuable data from the majority class. Option B, k-fold cross-validation, is a method for validating model performance but doesn't address the imbalance directly. Option C, tuning hyperparameters, improves model performance but does not specifically address the issue of class imbalance.