MLS-C01 Modeling Practice Question
A company is building a recommendation system using matrix factorization. The dataset has 1 million users and 100,000 items. The data scientist trains a model using SageMaker's Factorization Machines algorithm. The model achieves a root mean squared error (RMSE) of 0.95 on the test set. However, the business requires RMSE below 0.90. The data scientist has already tuned hyperparameters like number of factors and learning rate. Which additional step should the data scientist take to improve RMSE?
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 side features such as user demographics and item categories
(add side features) provides more information to the model, which can help reduce RMSE. Option B (more iterations) may not help if the model has already converged. Option C (increasing factors to 1000) could lead to overfitting. Option D (linear regression) is less powerful than matrix factorization for recommendation tasks.
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 side features such as user demographics and item categories
Why this is correct
Side features enrich the model and can improve accuracy.
- ✗
Increase the number of training iterations
Why it's wrong here
If converged, more iterations won't help.
- ✗
Increase the number of factors to 1000
Why it's wrong here
More factors can lead to overfitting and slower training.
- ✗
Use a linear regression model instead
Why it's wrong here
Linear regression cannot model interactions well.
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