AWS Certified Machine Learning – Specialty — Question 269
A machine learning (ML) specialist is training a linear regression model. The specialist notices that the model is overfitting. The specialist applies an L1 regularization parameter and runs the model again. This change results in all features having zero weights.
What should the ML specialist do to improve the model results?
Answer options
- A. Increase the L1 regularization parameter. Do not change any other training parameters.
- B. Decrease the L1 regularization parameter. Do not change any other training parameters.
- C. Introduce a large L2 regularization parameter. Do not change the current L1 regularization value.
- D. Introduce a small L2 regularization parameter. Do not change the current L1 regularization value.
Correct answer: B
Explanation
L1 regularization (Lasso) penalizes the absolute magnitude of the weights, which can drive some coefficients to exactly zero. If the L1 regularization parameter is set too high, the penalty becomes too severe, causing all feature weights to drop to zero and leading to underfitting. Decreasing the L1 parameter reduces this penalty, allowing the model to retain critical feature weights while still mitigating overfitting.