MLS-C01 Modeling Practice Question
A company is using SageMaker built-in object detection algorithm to detect defects in manufacturing images. The model is trained on 10,000 labeled images and achieves 95% accuracy. However, in production, the model misclassifies many defective items as non-defective (false negatives). The business requires recall > 90% for the defect class. Which action should they take?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Adjust the decision threshold of the model to increase recall at the expense of precision
Threshold tuning directly optimizes recall for a given class.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a different algorithm such as semantic segmentation
Why it's wrong here
Changing algorithm may not directly address recall.
- ✓
Adjust the decision threshold of the model to increase recall at the expense of precision
Why this is correct
Lowering the threshold increases recall for the positive class.
- ✗
Use SageMaker's Automatic Model Tuning to find better hyperparameters
Why it's wrong here
Tuning may help but threshold adjustment is the most direct way.
- ✗
Retrain the model with more images of non-defective items
Why it's wrong here
This may worsen the imbalance and reduce recall.
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