MLS-C01 Modeling Practice Question
A data scientist is building a regression model to predict house prices. The dataset includes features such as square footage, number of bedrooms, year built, and location. After training a linear regression model, the data scientist notices that the residuals have a clear pattern when plotted against predicted values: they increase with predicted values. The model also has high RMSE. Which action should the data scientist take to improve the 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
✓
Apply a log transformation to the target variable.
A pattern in residuals indicates non-linearity, and transforming the target variable (e.g., log transformation) can stabilize variance and linearize relationships. Option A is wrong because removing outliers does not address the underlying non-linearity or heteroscedasticity; it may even discard useful data. Option B is wrong because L1 regularization helps reduce overfitting by penalizing large coefficients, but it does not fix non-constant variance or non-linearity. Option D is wrong because while interaction terms can model relationships between features, they do not directly address the pattern of increasing residuals (heteroscedasticity) and may not resolve the non-linearity in the target.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove outliers from the dataset.
Why it's wrong here
Outliers may contribute to the pattern but the pattern suggests a systematic issue, not outliers.
- ✗
Use L1 regularization (Lasso) to reduce overfitting.
Why it's wrong here
Regularization addresses overfitting, not the non-linear pattern in residuals.
- ✓
Apply a log transformation to the target variable.
Why this is correct
Log transformation can stabilize variance and linearize the relationship, reducing the residual pattern.
- ✗
Add interaction terms between features.
Why it's wrong here
Interaction terms capture feature interactions but do not address the residual pattern indicating non-linearity.
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