AI-102 Implement computer vision solutions Practice Question
A hospital uses Azure Custom Vision to classify X-ray images as normal or abnormal. The model achieves 98% accuracy on the test set. However, during deployment, the model misclassifies many abnormal cases as normal, causing missed diagnoses. The hospital has a class imbalance where abnormal cases are only 5% of the data. What should the data scientist do first to address this?
⚠ Common exam trap
Many exam-takers confuse accuracy with model effectiveness, assuming high test accuracy (98%) means the model is robust, but they overlook that accuracy is misleading with severe class imbalance—a model predicting 'normal' for every image would achieve 95% accuracy while missing all abnormal cases.
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
✓
Use oversampling or class-weight techniques to balance the training.
The primary issue is class imbalance, where abnormal cases constitute only 5% of the data. Oversampling (e.g., SMOTE) or class-weight techniques adjust the training process to give more importance to the minority class, directly addressing the model's bias toward the majority class and reducing false negatives. This is a standard preprocessing step in Custom Vision and other ML frameworks before tuning hyperparameters or changing algorithms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of training epochs.
Why it's wrong here
More epochs may lead to overfitting and not address imbalance.
- ✗
Add more normal X-ray images to the dataset.
Why it's wrong here
Adding more normal images worsens the imbalance.
- ✗
Switch to a different object detection algorithm.
Why it's wrong here
The algorithm is not the root cause; imbalance is.
- ✓
Use oversampling or class-weight techniques to balance the training.
Why this is correct
Balancing the dataset or adjusting loss weights improves minority class recall.
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