MLS-C01 Modeling Practice Question
A data scientist is training a deep learning model for image classification. The model is overfitting on the training data. Which combination of techniques will most effectively reduce overfitting?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that increasing model complexity (more layers/neurons) or training longer will fix overfitting, when in reality these actions worsen it, and that simple hyperparameter changes like batch size reduction are not primary regularization techniques.
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 and use data augmentation
Dropout layers randomly deactivate a fraction of neurons during training, which forces the network to learn more robust features and prevents co-adaptation. Data augmentation artificially expands the training dataset by applying transformations (e.g., rotation, flipping, cropping), which reduces the model's ability to memorize spurious patterns and improves generalization. Together, these techniques directly counteract overfitting by increasing regularization and effective training diversity.
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 and use data augmentation
Why this is correct
Dropout randomly drops units to prevent co-adaptation; data augmentation increases effective training set size, both reduce overfitting.
- ✗
Reduce the batch size
Why it's wrong here
Reducing batch size introduces noise but is not a primary method to reduce overfitting.
- ✗
Train for more epochs without early stopping
Why it's wrong here
More training without regularization increases overfitting.
- ✗
Increase the number of layers and neurons
Why it's wrong here
Increasing model complexity exacerbates overfitting.
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