You are responsible for an Azure AI solution that uses Custom Vision to classify manufacturing defects. The model must achieve high recall to avoid missing defects. The current model has high precision but low recall. Which action should you take?
Lowering the threshold increases the number of positive predictions, thus improving recall.
Why this answer
Lowering the probability threshold for the defect class means the model will classify an image as defective even when its confidence score is lower. This increases the number of true positives (defects caught), directly improving recall at the cost of potentially more false positives. In Custom Vision, the default probability threshold is 50%, and adjusting it downward is the standard technique to prioritize recall over precision.
Exam trap
The trap here is that candidates confuse precision and recall, often assuming that increasing the threshold (making the model stricter) will improve overall performance, when in fact it reduces recall by missing more defects.
How to eliminate wrong answers
Option A is wrong because adding more images of non-defective items would bias the model toward the non-defective class, likely reducing recall further by making the model more conservative in predicting defects. Option B is wrong because increasing the number of training iterations (epochs) primarily helps the model converge better on the training data but does not directly control the precision-recall trade-off; it may even lead to overfitting without improving recall. Option D is wrong because increasing the probability threshold for the defect class would require higher confidence to classify a defect, which reduces false positives but also reduces true positives, thereby lowering recall even further.