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A data scientist has built an image recognition model that distinguishes cars from trucks. The data scientist now wants to measure the rate at which the model correctly identifies a car as a car versus when it misidentifies a truck as a car. Which of the following would best convey this information?
Correct Answer: A
# 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." -
