MLS-C01 Modeling Practice Question
A data scientist is building a regression model to predict housing prices. The dataset includes numerical features such as square footage, number of bedrooms, and year built, as well as categorical features such as neighborhood and roof type. Which TWO preprocessing steps are most important to apply 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
Linear regression requires numerical features and is sensitive to feature scales. Encoding categorical variables as numerical is necessary, and scaling numerical features ensures that no single feature dominates the model.
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 principal component analysis (PCA) for dimensionality reduction
Why it's wrong here
PCA is not typically necessary and may remove interpretability; it's not a required preprocessing step.
- ✓
One-hot encode categorical features
Why this is correct
One-hot encoding converts categorical variables into numerical form suitable for linear regression.
- ✗
Remove outliers using IQR
Why it's wrong here
Outlier removal is dataset-specific and not always required; it depends on the data.
- ✗
Add interaction terms between all features
Why it's wrong here
Interaction terms are optional and can lead to overfitting; not a mandatory preprocessing step.
- ✓
Normalize or standardize numerical features
Why this is correct
Scaling numerical features ensures that features with larger scales do not dominate the regression coefficients.
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