MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a linear regression model. The training data has 10 features, and the scientist wants to interpret the model's coefficients. Which algorithm should they use?
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
✓
Amazon SageMaker Linear Learner
Amazon SageMaker Linear Learner provides interpretable coefficients, which is essential for understanding the impact of each feature in a linear regression model. Option A is wrong because XGBoost is a tree-based ensemble method that is less interpretable and does not provide linear coefficients. Option B is wrong because K-Means is an unsupervised clustering algorithm, not suited for regression. Option C is wrong because Factorization Machines are designed for high-dimensional sparse data and are not the standard choice for linear regression with 10 features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon SageMaker XGBoost
Why it's wrong here
Tree-based, less interpretable.
- ✗
Amazon SageMaker K-Means
Why it's wrong here
Unsupervised clustering.
- ✗
Amazon SageMaker Factorization Machines
Why it's wrong here
Used for high-dimensional sparse data.
- ✓
Amazon SageMaker Linear Learner
Why this is correct
Produces linear coefficients for interpretation.
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