MLS-C01 Modeling Practice Question
A data scientist is training a linear regression model and notices high bias in the training set. What action is most likely to reduce bias?
⚠ Common exam trap
Many candidates confuse high bias with high variance and incorrectly choose increasing training data (Option C) or regularization (Option A), which are solutions for overfitting, not underfitting.
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 to the model.
High bias indicates that the model is underfitting the training data, meaning it is too simple to capture the underlying patterns. Adding more relevant features increases the model's capacity to learn complex relationships, directly reducing bias. This is a standard approach in linear regression to address underfitting.
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.
Why it's wrong here
Regularization reduces overfitting (variance), not bias.
- ✗
Increase the learning rate.
Why it's wrong here
Learning rate affects training speed, not bias.
- ✗
Increase the amount of training data.
Why it's wrong here
More data typically reduces variance, not bias.
- ✓
Add more relevant features to the model.
Why this is correct
Adding features increases model capacity, which can reduce high bias.
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