MLS-C01 Modeling Practice Question
A data scientist is training a binary classification model on a dataset with 10,000 features. The model overfits severely. Which technique is MOST appropriate to reduce overfitting?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that dimensionality reduction (PCA) is always the best solution for high-dimensional overfitting, but L1 regularization is more direct because it performs feature selection within the model itself, preserving interpretability and avoiding information loss from linear projections.
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 L1 regularization (Lasso)
With 10,000 features, L1 regularization (Lasso) is the most appropriate technique because it performs feature selection by shrinking less important feature coefficients to exactly zero. This directly addresses the high-dimensional overfitting by reducing model complexity and removing noise features, which is more effective than other methods for such a large feature space.
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 L1 regularization (Lasso)
Why this is correct
L1 regularization penalizes the absolute size of coefficients, driving some to zero and reducing overfitting.
- ✗
Use early stopping during training
Why it's wrong here
Early stopping helps but is less effective than regularization for high-dimensional data.
- ✗
Use PCA to reduce dimensionality
Why it's wrong here
PCA reduces dimensionality but does not directly address overfitting; it may discard useful features.
- ✗
Increase the max depth of the model
Why it's wrong here
Increasing max depth typically increases overfitting.
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