MLS-C01 Modeling Practice Question
A retail company uses Amazon SageMaker to train a model for product demand forecasting. The dataset contains daily sales data for 10,000 products over 3 years. The data includes features like price, promotions, holidays, and seasonality. The data scientist uses a linear regression model and gets an RMSE of 50 units. However, the business requires more accurate forecasts, especially for products with high variability. The scientist notices that the residuals show a pattern: the model underestimates demand during promotional periods. Which approach should the 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
✓
Add interaction features between promotion and other variables.
Adding interaction features between promotion and other variables allows the model to capture the specific effect of promotions on demand, which the linear regression currently underestimates. Option B (more data) may help but won't directly address the structural bias; Option C (LSTM) might be overkill and not directly solve the underestimation during promotions; Option D (removing promotion features) would worsen the problem by discarding valuable information.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Add interaction features between promotion and other variables.
Why this is correct
Interaction terms capture combined effects.
- ✗
Collect more historical data for training.
Why it's wrong here
Does not address the systematic bias during promotions.
- ✗
Use a deep learning model like LSTM.
Why it's wrong here
May not be necessary and could overfit.
- ✗
Remove promotion features to simplify the model.
Why it's wrong here
Removes valuable signal.
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