A data scientist has built an image recognition model that distinguishes cars from trucks.

→ A confusion matrix gives a detailed view of a classification model’s performance, including true positives, false positives, true negatives, and false negatives. It’s the best tool for examining model accuracy and misclassification between specific classes — like mislabeling trucks as cars.

Why the other options are incorrect:

B: AUC/ROC gives a broader performance summary but not individual class misclassifications.

C: Box plots show distributions, not classification accuracy.

D: Correlation plots show relationships between variables — not confusion results.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 4.3:“Confusion matrices enable detailed analysis of classification performance and misclassification rates.”

Machine Learning Textbook, Chapter 5:“For evaluating how models classify specific classes, confusion matrices are the most direct and interpretable tool.”

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