MLS-C01 Modeling Practice Question
A data scientist is tuning a neural network on a small dataset and observes that the training loss decreases but validation loss increases after a few epochs. Which technique should be applied to mitigate this issue?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that increasing epochs always improves performance, but the trap here is that candidates may overlook overfitting and choose to increase epochs, not realizing that validation loss divergence signals the need for regularization instead.
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
✓
Add dropout layers to the model.
The observed behavior—training loss decreasing while validation loss increasing—is a classic sign of overfitting. Adding dropout layers randomly drops a fraction of neurons during training, which forces the network to learn more robust features and reduces co-adaptation, thereby mitigating overfitting. This technique is specifically designed to improve generalization on small datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Add dropout layers to the model.
Why this is correct
Dropout randomly drops neurons, reducing overfitting.
- ✗
Increase the learning rate.
Why it's wrong here
Increasing learning rate can cause divergence, not fix overfitting.
- ✗
Remove regularization terms from the loss function.
Why it's wrong here
Removing regularization would increase overfitting.
- ✗
Increase the number of epochs.
Why it's wrong here
More epochs would worsen overfitting.
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