Classification Metrics — AWS AI Practitioner (AIF-C01) Practice Questions
Classification metrics are quantitative measures used to evaluate how well a model distinguishes between categories, such as accuracy, precision, recall, and the area under the ROC curve. AIF-C01 tests the ability to interpret a confusion matrix and choose the right metric given business context, for example preferring recall when false negatives are costly (medical screening) versus precision when false positives are costly (spam filtering). Understanding how class imbalance affects these metrics and how to address it is also in scope.
Free questions on classification metrics
Which metric is most appropriate for evaluating a multi-class classification model?
Free question · medium · full answer + explanation
More classification metrics questions in the full bank
- What is the purpose of model evaluation metrics like precision and recall? Unlock answer & explanation →
- What is "recall" in classification? Unlock answer & explanation →
- What is the primary purpose of a confusion matrix in classification tasks? Unlock answer & explanation →