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Answer: precision, accuracy
For binary classification models, the appropriate evaluation metrics include Accuracy, Precision, Recall, F1 Score, and AUC. Among the given options, B (Precision) and C (Accuracy) are standard metrics for binary classification. Precision measures the proportion of true positives among predicted positives, while Accuracy measures the overall correctness of the model. Options A (relative absolute error), D (mean absolute error), and E (coefficient of determination) are regression metrics, not suitable for binary classification tasks. The community discussion and answer description confirm that Precision and Accuracy are correct, with high upvotes supporting this consensus.
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You are creating a binary classification model and need to evaluate its performance. Which two metrics should you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A
relative absolute error
B
precision
C
accuracy
D
mean absolute error
E
coefficient of determination