DA0-002 Data Analysis Practice Question
A data analyst at a retail company is building a multiple linear regression model to forecast weekly sales. The dataset contains 50 predictor variables, including store size, promotional spend, holiday indicators, and many others. After training the model, the analyst observes an R-squared of 0.99 on the training set but only 0.55 on the holdout test set. Which action should the analyst take first to address this discrepancy?
⚠ Common exam trap
Many candidates think a high R-squared is always good, or they may confuse overfitting with underfitting and choose to add more complexity (Option D) or more data (Option B), rather than recognizing the need to reduce model complexity and apply regularization.
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
✓
Remove highly correlated predictor variables and apply regularization (e.g., Ridge or Lasso).
The high R-squared of 0.99 on training data versus 0.55 on test data is a classic sign of overfitting, where the model has learned noise and specific patterns in the training set that do not generalize. Removing highly correlated predictors reduces multicollinearity and model complexity, while regularization (Ridge or Lasso) penalizes large coefficients, shrinking them to prevent overfitting. This is the most direct first step to improve generalization.
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 highly correlated predictor variables and apply regularization (e.g., Ridge or Lasso).
Why this is correct
Regularization and feature selection reduce overfitting by penalizing large coefficients and removing redundant predictors.
- ✗
Add more predictor variables to increase the training R-squared further.
Why it's wrong here
Adding more predictors will likely worsen overfitting, not fix it.
- ✗
Use k-fold cross-validation with a different random seed to get a more reliable test set estimate.
Why it's wrong here
Cross-validation changes evaluation but does not directly fix overfitting; the model itself needs adjustment.
- ✗
Increase the number of hidden layers in the model to capture more complexity.
Why it's wrong here
Linear regression does not have hidden layers; this is irrelevant for a linear model.
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