MLS-C01 Modeling Practice Question
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.)
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
✓
Use a subset of the most important features
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).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normalize the features
Why it's wrong here
Normalization helps with convergence but does not directly reduce overfitting.
- ✗
Add more features to the model
Why it's wrong here
Adding features increases model complexity and overfitting.
- ✓
Use a subset of the most important features
Why this is correct
Reducing the number of features reduces model complexity and overfitting.
- ✗
Increase the number of training epochs
Why it's wrong here
Linear regression does not use epochs; it has a closed-form solution.
- ✓
Apply L2 regularization (Ridge regression)
Why this is correct
L2 regularization penalizes large coefficients, reducing overfitting.
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Same concept, more angles
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Variation 1. A data scientist is training a linear regression model on a dataset with 10 features. After training, the model has high variance on the test set. Which technique should the data scientist use to reduce variance without significantly increasing bias?
easy- ✓ A.Use L2 regularization
- B.Add more features
- C.Use a simpler model
- D.Use a deeper decision tree
Why A: L2 regularization (Ridge regression) adds a penalty term proportional to the square of the magnitude of the coefficients, which shrinks them toward zero. This reduces model complexity and variance by preventing any single feature from having an overly large influence, without eliminating features entirely, thus keeping bias relatively low.
JA
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Senior Network & Security Engineer · founder of Courseiva
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