Question 1,607 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 samples. The model achieves 99% accuracy on the training set but only 65% on the test set. Which technique should be applied first to address this issue?
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 to the model
The symptoms indicate overfitting. Regularization (L1/L2) is a direct method to reduce overfitting by penalizing large coefficients. Option A is wrong because reducing the size of the training dataset would worsen overfitting. Option B is wrong because increasing the number of trees in a random forest could help reduce overfitting in some cases, but it's not the first technique to apply; regularization is more direct. Option D is wrong because adding more features would increase model complexity and worsen overfitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the size of the training dataset
Why it's wrong here
Reducing data makes overfitting worse because the model has less to learn from.
- ✗
Increase the number of trees in a random forest
Why it's wrong here
Increasing trees may not help if overfitting is due to high variance; more trees can reduce variance but not if the base trees are deep.
- ✓
Apply L2 regularization to the model
Why this is correct
L2 regularization penalizes large weights, reducing overfitting.
- ✗
Add more features to the model
Why it's wrong here
Adding more features increases model complexity and likely worsens overfitting.
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Last reviewed: Jun 20, 2026
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