MLS-C01 Modeling Practice Question
A research team is developing a deep learning model to classify medical images into 10 disease categories. They have a dataset of 50,000 labeled images, but the class distribution is highly imbalanced: the most common class has 20,000 images, while the rarest class has only 200 images. To address this, they apply data augmentation (random rotations, flips, and brightness adjustments) to the minority classes until each class has 20,000 images. They then train a convolutional neural network (CNN) from scratch using cross-entropy loss. The model achieves 95% overall accuracy but only 30% recall on the rarest class. Which change is MOST likely to improve recall on the rarest class without significantly reducing overall accuracy?
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between regularization techniques (dropout, batch size) and loss function modifications (focal loss) for class imbalance, trapping candidates who think overfitting is the primary issue when the real problem is the model's bias toward majority classes.
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
✓
Replace cross-entropy loss with focal loss
Focal loss is specifically designed to address class imbalance by down-weighting the loss contribution from well-classified examples (majority classes) and focusing training on hard, misclassified examples (minority classes). This directly improves recall on the rarest class, while cross-entropy loss treats all classes equally, causing the model to be biased toward the majority classes.
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 dropout rate from 0.2 to 0.5 to reduce overfitting
Why it's wrong here
Higher dropout prevents overfitting but does not address class imbalance directly.
- ✓
Replace cross-entropy loss with focal loss
Why this is correct
Focal loss reduces the loss contribution from easy examples and focuses on hard, minority examples, improving recall.
- ✗
Switch from Adam optimizer to SGD with momentum
Why it's wrong here
Optimizer choice affects convergence speed but does not directly address class imbalance.
- ✗
Reduce the batch size from 64 to 16 to increase stochasticity
Why it's wrong here
Smaller batch size can help escape local minima but does not specifically improve recall on rare classes.
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