MLS-C01 Exploratory Data Analysis Practice Question
A data scientist uses Amazon SageMaker Data Wrangler to explore a dataset. The target column is 'price' (continuous). Which EDA analysis would best help decide between linear regression and tree-based models?
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
✓
Check linear relationships between features and target
Checking linear relationships (e.g., scatter plots of features vs. target) helps determine whether linear regression is appropriate or if tree-based models (which capture non-linear patterns) would perform better. Option A (VIF) is used to detect multicollinearity, which affects linear regression but does not directly guide model selection between linear and tree models. Option C (Z-score) identifies outliers, which is important but not the primary factor for deciding between these model types. Option D (class imbalance) is relevant for classification problems, not regression.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Compute variance inflation factor (VIF) for features
Why it's wrong here
VIF is used to detect multicollinearity, which affects linear regression but does not directly guide model selection between linear and tree models.
- ✓
Check linear relationships between features and target
Why this is correct
Checking linear relationships (e.g., scatter plots of features vs. target) helps determine whether linear regression is appropriate or if tree-based models (which capture non-linear patterns) would perform better.
- ✗
Detect outliers using Z-score
Why it's wrong here
Z-score identifies outliers, which is important but not the primary factor for deciding between these model types.
- ✗
Identify class imbalance in the target
Why it's wrong here
Class imbalance is relevant for classification problems, not regression.
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