MLS-C01 Modeling Practice Question
A data scientist is training a linear regression model on a dataset with 50 features. After training, they notice that the model performs well on training data but poorly on test data. They suspect overfitting. Which action should they take to reduce overfitting?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that adding more data or training longer always helps, but the trap here is that overfitting is a variance problem best addressed by regularization or reducing model complexity, not by extending training or adding features.
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 (Ridge regression)
L2 regularization (Ridge regression) adds a penalty term proportional to the square of the magnitude of the coefficients to the loss function. This discourages the model from fitting the noise in the training data by shrinking the weights, which reduces variance and mitigates overfitting, improving generalization to the test data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a larger learning rate
Why it's wrong here
Learning rate does not directly address overfitting.
- ✓
Add L2 regularization (Ridge regression)
Why this is correct
L2 regularization penalizes large coefficients, reducing overfitting.
- ✗
Add more features to the model
Why it's wrong here
More features can increase overfitting.
- ✗
Increase the number of training epochs
Why it's wrong here
More epochs can increase overfitting.
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