MLS-C01 Modeling Practice Question
A machine learning engineer is training a regression model to predict house prices using Amazon SageMaker. The dataset contains 10,000 samples and 50 numerical features. After training a linear regression model, the engineer notices that the training loss is low, but the validation loss is high. The engineer suspects overfitting. The dataset is already normalized. Which action should the engineer take to reduce overfitting?
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 L2 regularization (weight decay) to the loss function.
L2 regularization (weight decay) adds a penalty term to the loss function that discourages large weight values, effectively reducing model complexity and overfitting. Option A (increasing learning rate) can cause the model to diverge or overshoot minima, and does not directly prevent overfitting. Option B (PCA) reduces the number of features, which can help with overfitting but may discard important information; regularization is a more targeted approach for linear models. Option D (decreasing mini-batch size) introduces more noise into gradient estimates, which can sometimes act as a regularizer but is less effective and reliable than L2 regularization for this scenario.
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 learning rate to speed up convergence.
Why it's wrong here
Incorrect: Higher learning rate may overshoot minima and worsen performance.
- ✗
Reduce the number of features using PCA.
Why it's wrong here
PCA reduces dimensionality by projecting features onto orthogonal components that maximise variance, but this does not directly address overfitting caused by the model memorising noise in the training data. The engineer already has normalised numerical features, and PCA may discard information relevant to house price prediction while failing to penalise large coefficients. It is tempting because PCA is commonly used to simplify high-dimensional datasets and can reduce variance in some models, but here regularisation (e.g., L2) would directly constrain coefficient magnitudes to combat overfitting.
- ✓
Add L2 regularization (weight decay) to the loss function.
Why this is correct
Correct: L2 regularization penalizes large weights and reduces overfitting.
- ✗
Decrease the mini-batch size during training.
Why it's wrong here
Incorrect: Smaller batch size adds noise but is not a standard regularization technique.
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