MLS-C01 Modeling Practice Question
A data scientist is training a regression model on a dataset with 50 features. After training a linear regression model, the model achieves an R-squared of 0.85 on the training set but only 0.55 on the test set. Which technique is most likely to reduce the generalization error?
⚠ Common exam trap
AWS often tests the distinction between overfitting (high variance) and underfitting (high bias), and candidates mistakenly choose feature removal or polynomial adjustment when regularization is the direct fix for variance-dominated error.
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 regression)
The model exhibits high variance (overfitting): high training R² (0.85) but much lower test R² (0.55). L2 regularization (Ridge regression) shrinks coefficients toward zero, reducing model complexity and penalizing large weights, which directly combats overfitting and improves generalization to unseen 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.
- ✗
Add more features
Why it's wrong here
Adding more features increases model complexity, likely worsening overfitting.
- ✗
Remove highly correlated features
Why it's wrong here
While correlated features can cause instability, regularization is a more direct remedy for overfitting in this scenario.
- ✗
Increase the polynomial degree of the model
Why it's wrong here
Increasing polynomial degree adds complexity, likely increasing overfitting.
- ✓
Apply L2 regularization (Ridge regression)
Why this is correct
L2 regularization shrinks coefficients, reducing variance and improving test performance.
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Same concept, more angles
1 more way this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is training a linear regression model on a dataset with 10 numerical features. After training, the model's R-squared value is 0.99 on the training set but only 0.60 on the test set. Which TWO of the following are appropriate actions to reduce overfitting? (Choose TWO.)
medium- A.Normalize the features
- B.Add more features to the model
- ✓ C.Use a subset of the most important features
- D.Increase the number of training epochs
- ✓ E.Apply L2 regularization (Ridge regression)
Why C: Regularization (L1 or L2) penalizes large coefficients and reduces overfitting. Reducing model complexity by using fewer features or simplifying the model also helps. Adding more features would increase complexity and overfitting. Increasing the number of epochs is not relevant for linear regression (which has a closed-form solution).
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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