AI Associate AI Fundamentals Practice Question
A data scientist notices that a churn prediction model has high variance: small changes in training data cause large changes in predictions. Which technique is BEST to address this?
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 regularization techniques
High variance indicates overfitting. Regularization (e.g., L1/L2) reduces model complexity and variance. Adding more features would increase variance, increasing training data can help but is not always feasible, and reducing training data would worsen the problem.
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 regularization techniques
Why this is correct
Regularization penalizes large coefficients, reducing model complexity and variance.
- ✗
Reduce the amount of training data
Why it's wrong here
Less data typically increases variance, not decreases it.
- ✗
Add more features to the model
Why it's wrong here
More features typically increase variance, worsening the problem.
- ✗
Increase the number of training epochs
Why it's wrong here
More epochs can lead to overfitting, increasing variance.
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