MLS-C01 Modeling Practice Question
A data scientist is training a linear regression model and observes that the training loss is low but validation loss is high. Which step should the data scientist take 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 model is overfitting (low training loss, high validation loss). L2 regularization adds a penalty on the magnitude of coefficients, which discourages complexity and reduces overfitting. Increasing training epochs (B) would likely worsen overfitting by allowing the model to memorize more. Reducing the training dataset size (C) would provide less data, making overfitting worse. Adding more features (D) increases model complexity and typically exacerbates 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.
- ✓
Apply L2 regularization to the model
Why this is correct
Regularization penalizes large weights, reducing overfitting.
- ✗
Increase the number of training epochs
Why it's wrong here
More epochs can lead to overfitting.
- ✗
Reduce the size of the training dataset
Why it's wrong here
Reducing data can exacerbate overfitting.
- ✗
Add more features to the model
Why it's wrong here
Adding features may increase model complexity and overfitting.
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