MLS-C01 Modeling Practice Question
A data scientist is training a linear regression model and notices that the model performs well on training data but poorly on validation data. Which technique should be applied to reduce overfitting?
⚠ Common exam trap
Many exam-takers confuse regularization with techniques that increase model capacity (like adding features or more training iterations), not realizing that overfitting requires reducing complexity, not increasing it.
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 L2 regularization (Ridge)
L2 regularization (Ridge) adds a penalty term proportional to the square of the magnitude of the coefficients to the loss function. This shrinks the weights toward zero, reducing the model's sensitivity to individual features and preventing it from fitting noise in the training data, which directly addresses overfitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Apply L2 regularization (Ridge)
Why this is correct
Regularization reduces overfitting by penalizing large coefficients.
- ✗
Increase the number of epochs
Why it's wrong here
More epochs can overfit more.
- ✗
Add more features
Why it's wrong here
More features can increase overfitting.
- ✗
Remove training examples
Why it's wrong here
Reducing data can worsen overfitting.
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