MLS-C01 Modeling Practice Question
A data scientist is training a neural network for image classification. The dataset has 50,000 images across 100 classes. The model uses a ResNet-50 architecture pre-trained on ImageNet. The training loss decreases rapidly, but validation loss starts to increase after 5 epochs. Which of the following is the most effective technique to address this?
⚠ Common exam trap
Many candidates confuse overfitting with underfitting or training instability, and incorrectly choose to increase learning rate or add layers, not recognizing that the validation loss rising while training loss falls is the textbook symptom of overfitting that requires regularization or more data.
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 data augmentation to increase the diversity of the training set
The rapid decrease in training loss followed by an increase in validation loss after only 5 epochs is a classic sign of overfitting. Data augmentation artificially expands the training set by applying random transformations (e.g., rotations, flips, crops) to existing images, which improves the model's generalization and reduces overfitting. This is the most effective technique among the options because it directly addresses the lack of diverse training examples without changing the model architecture or training hyperparameters in a way that could destabilize learning.
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 learning rate
Why it's wrong here
Increasing learning rate may cause divergence and does not address overfitting.
- ✗
Add more layers to the network
Why it's wrong here
Adding layers increases model capacity and likely worsens overfitting.
- ✓
Use data augmentation to increase the diversity of the training set
Why this is correct
Data augmentation artificially expands the training set, reducing overfitting and improving generalization.
- ✗
Use a smaller batch size
Why it's wrong here
Smaller batch sizes can introduce noise but are not the most effective for overfitting in this scenario.
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