The question is about evaluating a Custom Vision classifier after training.
When you train an image classifier in Azure Custom Vision, the service automatically calculates performance metrics to help you evaluate the quality of the model.
Metrics Provided by Custom Vision:
Precision:
The percentage of correct positive predictions out of all positive predictions made.
Formula: Precision = TP / (TP + FP)
Helps determine how reliable positive predictions are.
Recall:
The percentage of actual positives that were correctly predicted.
Formula: Recall = TP / (TP + FN)
Helps determine how many of the true positives were captured.
These two metrics are explicitly shown in the Custom Vision portal and via API.
Option Analysis
A. Recall
Yes, Custom Vision reports recall.
Correct
B. F-score
Not directly reported in the Custom Vision portal.
Although it can be derived from precision and recall, it is not provided as a direct metric.
Incorrect
C. Weighted accuracy
Not reported by Custom Vision.
Incorrect
D. Precision
Yes, Custom Vision reports precision.
Correct
E. Area under the curve (AUC)
Not reported by Custom Vision.
More common in ROC curve analysis, not part of Custom Vision output.
Incorrect
Correct Answer: A. recall, D. precision
Microsoft References
Evaluate the prediction performance of your classifier in Custom Vision
Custom Vision training and evaluation
