MLS-C01 Modeling Practice Question
A machine learning engineer is training a linear regression model on a dataset with 50 features. After training, the model achieves high accuracy on the training set but poor accuracy on the test set. Which technique should the engineer use to address this issue?
⚠ Common exam trap
AWS often tests the distinction between overfitting and underfitting, and the trap here is that candidates may think adding more data (Option D) is the universal fix for overfitting, when in fact regularization is the most direct and efficient solution for a model with high variance.
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
✓
Apply L1 or L2 regularization
The model exhibits overfitting: high training accuracy but poor test accuracy. L1 (Lasso) or L2 (Ridge) regularization penalizes large coefficients, reducing model complexity and improving generalization. This directly addresses the variance problem without requiring more data or features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Train a deeper neural network with more layers
Why it's wrong here
Increasing model depth increases capacity and the risk of overfitting, which exacerbates the current discrepancy between training and test performance. This approach addresses underfitting by allowing a network to learn non-linear relationships in high-dimensional data where existing layers fail to capture the underlying patterns. While adding layers helps when a model lacks sufficient complexity to map input features to targets, it fails here because the engineer must reduce variance through regularisation or dimensionality reduction.
- ✗
Add more features through feature engineering
Why it's wrong here
Adding features can increase overfitting.
- ✓
Apply L1 or L2 regularization
Why this is correct
Regularization penalizes large coefficients and reduces overfitting.
- ✗
Increase the size of the training dataset
Why it's wrong here
More data helps but is not always available and is not a direct regularization technique.
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