Explanation

Answer Example

I would evaluate the model using metrics like precision, recall, and F1-score, especially in cases of class imbalance. For example, in a churn prediction model, I prioritized recall to minimize false negatives, as missing a potential churner is costlier than a false positive. Additionally, I would use ROC-AUC to assess the model's overall ability to distinguish between classes and analyze the confusion matrix to understand specific errors. This approach ensures the evaluation aligns with business objectives and problem context.