MLS-C01 Modeling Practice Question
A company is building a sentiment analysis model for customer reviews. The dataset is balanced with 10,000 positive and 10,000 negative reviews. The model achieves 95% accuracy on the test set but fails to generalize to new reviews from a different product category. Which TWO techniques can improve generalization?
⚠ Common exam trap
It's easy for candidates to assume increasing data size or model complexity always improves generalization, but the question specifically tests the understanding that cross-validation techniques like stratified k-fold directly address overfitting and domain shift by providing a more reliable estimate of model performance across diverse data splits.
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 stratified k-fold cross-validation during training
Stratified k-fold cross-validation ensures that each fold maintains the same class distribution as the original dataset, which helps the model learn more robust patterns across different subsets of data. This technique reduces variance in the evaluation and improves generalization to unseen data from different product categories by preventing overfitting to idiosyncrasies of a single train-test split.
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 training dataset size by collecting more reviews
Why it's wrong here
More data helps, but the question asks for techniques that can be applied to the existing scenario; collecting more data may not be immediately possible.
- ✓
Use stratified k-fold cross-validation during training
Why this is correct
Cross-validation provides a more reliable estimate of generalization and helps tune hyperparameters.
- ✓
Apply L2 regularization to the model
Why this is correct
Regularization penalizes large weights and reduces overfitting.
- ✗
Add more features like review length and word count
Why it's wrong here
Adding more features may lead to overfitting if not carefully engineered.
- ✗
Use a more complex model with more layers
Why it's wrong here
More complexity increases the risk of overfitting.
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