MLS-C01 Modeling Practice Question
A machine learning team is developing a model to predict housing prices. They have a dataset with numerical features like square footage and number of bedrooms, and categorical features like neighborhood. Which preprocessing step is essential before training a linear regression model?
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
✓
One-hot encode categorical features
One-hot encoding converts categorical features into binary columns, which linear regression requires. Option A is wrong because scaling is important but not the only essential step; encoding is needed first. Option B is wrong because removing correlated features can help with multicollinearity but is not essential for linear regression. Option D is wrong because PCA reduces dimensionality but is optional and not a preprocessing step required before training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normalize all numerical features to have zero mean and unit variance
Why it's wrong here
Scaling is important but encoding categorical variables is equally essential.
- ✗
Remove highly correlated features
Why it's wrong here
Feature selection is optional and not essential for all linear regression models.
- ✓
One-hot encode categorical features
Why this is correct
Linear regression requires numerical input; one-hot encoding is needed for categorical variables.
- ✗
Apply Principal Component Analysis (PCA) to reduce dimensionality
Why it's wrong here
PCA is optional and not always necessary.
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