MLS-C01 Modeling Practice Question
A data scientist is building a classification model to predict customer churn. The dataset has 10,000 samples with 100 features. After training a logistic regression model, the scientist observes that the model has high variance (overfitting). Which technique can reduce overfitting?
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 L2 regularization (Ridge)
L2 regularization (Ridge) adds a penalty on large coefficients, reducing overfitting. Removing features may help but is not the best practice. Increasing model complexity (polynomial features) would worsen overfitting. Increasing training data helps but not listed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove the regularization term
Why it's wrong here
Removing regularization would increase overfitting.
- ✓
Use L2 regularization (Ridge)
Why this is correct
L2 regularization penalizes large weights, reducing overfitting.
- ✗
Add polynomial features
Why it's wrong here
Adding polynomial features increases model complexity, likely increasing overfitting.
- ✗
Use a smaller learning rate
Why it's wrong here
Using a smaller learning rate addresses issues with model convergence during training, such as instability or failure to reach an optimal solution efficiently, rather than directly mitigating overfitting. Overfitting in logistic regression stems from the model's capacity to memorise noise in the training data, leading to high variance. While a smaller learning rate helps ensure stable parameter updates in gradient descent, it does not inherently simplify the model or reduce its tendency to fit noise. It would be appropriate if the model was failing to converge or exhibiting erratic training behaviour.
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