Question 521 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a binary classification model on a dataset with 100 features and 10,000 rows. The model overfits significantly: training accuracy is 99%, but validation accuracy is 80%. The data scientist has tried L1 and L2 regularization without improvement. The dataset is clean and representative. Which approach is MOST likely to reduce overfitting? A. Increase the number of training epochs. B. Add more training data by generating synthetic samples using SMOTE. C. Reduce the number of features using PCA. D. Use a simpler model like logistic regression instead of a decision tree ensemble. The data scientist needs to maintain a validation accuracy above 85%, but the current model is too complex. The company has limited budget for data labeling. Which option is BEST?
⚠ Common exam trap
A common mix-up: candidates assume more data (SMOTE) or dimensionality reduction (PCA) will always reduce overfitting, but in this scenario the core issue is model complexity, not data quantity or feature noise.
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 simpler model like logistic regression
The current model (likely a decision tree ensemble like Random Forest or XGBoost) is too complex for the dataset, causing overfitting. Switching to a simpler model like logistic regression reduces variance by limiting the hypothesis space, which directly addresses overfitting without requiring additional data or feature engineering. Given the limited labeling budget, this approach is cost-effective and can improve generalization, potentially achieving the required >85% validation accuracy.
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 simpler model like logistic regression
Why this is correct
Simpler model reduces capacity and overfitting.
- ✗
Add more training data by generating synthetic samples using SMOTE
Why it's wrong here
Synthetic data may not help and can introduce noise.
- ✗
Reduce the number of features using PCA
Why it's wrong here
PCA may not reduce overfitting if the model is already regularized.
- ✗
Increase the number of training epochs
Why it's wrong here
More epochs often increase overfitting.
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Last reviewed: Jun 24, 2026
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