MLS-C01 Modeling Practice Question
A data scientist is building a regression model to predict energy consumption. The dataset includes features like temperature, humidity, day of week, and holiday flags. The scientist uses a linear regression model and obtains an R-squared of 0.85 on training and 0.40 on test. The scientist suspects the model is not capturing non-linear relationships. Which approach should the scientist use to capture non-linearity?
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 polynomial features (e.g., squared terms and interactions)
(add polynomial features) captures non-linear relationships by introducing squared terms and interactions, allowing the linear model to fit curvature. Option A (PCA) reduces dimensionality but does not add non-linearity. Option B (L1 regularization using Lasso) reduces overfitting by shrinking coefficients but does not introduce non-linear terms. Option C (removing features with low correlation) may lose information and does not help with non-linearity.
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 PCA to the feature set
Why it's wrong here
PCA is linear transformation, not capturing non-linearity.
- ✗
Increase L1 regularization using Lasso
Why it's wrong here
Regularization does not add non-linearity.
- ✗
Remove features with low correlation to the target
Why it's wrong here
Removing features may not help capture non-linearity.
- ✓
Add polynomial features (e.g., squared terms and interactions)
Why this is correct
Polynomial features allow linear model to fit non-linear patterns.
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