MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is training a linear regression model on a dataset with 100 features. The model shows high variance on the test set. Which action is MOST likely to reduce overfitting?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that adding complexity (more features, more iterations, or more powerful models) always improves performance, when in fact overfitting requires reducing model complexity through regularization or simpler models.
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)
High variance (overfitting) means the model is too complex and fits noise in the training data. L2 regularization (Ridge regression) adds a penalty proportional to the square of the coefficients, shrinking them toward zero and reducing model complexity. This directly counteracts overfitting by preventing the model from relying too heavily on any single feature.
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 more complex model like XGBoost
Why it's wrong here
Switching to XGBoost would increase model capacity through gradient-boosted ensemble trees, which typically exacerbates high variance rather than reducing it, because the stem already describes a model suffering from overfitting on 100 features. This option is tempting because XGBoost often outperforms linear models on complex, non-linear datasets where underfitting is the problem, and would be correct if the issue were high bias from an overly simple linear assumption.
- ✗
Increase the number of training iterations
Why it's wrong here
More iterations do not reduce overfitting; they may cause overfitting if not early stopped.
- ✓
Apply L2 regularization (Ridge regression)
Why this is correct
L2 regularization penalizes large coefficients, reducing overfitting.
- ✗
Add more feature engineering to increase model complexity
Why it's wrong here
Adding more features increases model complexity and can worsen overfitting.
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