MLS-C01 Modeling Practice Question
A data scientist is training a linear regression model. After training, the model has a high bias and low variance. Which technique should the data scientist use to reduce bias?
⚠ Common exam trap
The MLS-C01 exam often tests the bias-variance tradeoff by presenting regularization as a solution for high bias, but candidates must remember that regularization (L1/L2) primarily reduces variance, not bias, and can actually increase bias if applied too strongly.
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
✓
Add more relevant features
High bias indicates the model is underfitting the data, meaning it is too simple to capture the underlying patterns. Adding more relevant features increases model complexity, allowing it to learn more from the data and reduce bias. This directly addresses the underfitting issue without increasing variance excessively, provided the features are meaningful.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the model complexity
Why it's wrong here
Decreasing complexity increases bias.
- ✓
Add more relevant features
Why this is correct
Adding features increases model complexity and can reduce bias.
- ✗
Apply L2 regularization (Ridge)
Why it's wrong here
L2 regularization adds penalty and increases bias, not reduces it.
- ✗
Reduce the amount of training data
Why it's wrong here
Reducing training data typically increases bias and variance.
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