MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is working with a dataset that contains both numerical and categorical features. The target variable is continuous. Which TWO EDA techniques should the scientist use to understand relationships between features and the target?
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
✓
Create scatter plots of numerical features against the target variable.
Scatter plots (C) are appropriate for visualizing relationships between numerical features and a continuous target. Box plots (D) are appropriate for comparing target distribution across categories of categorical features. Option A (confusion matrix) is used for classification, not regression. Option B (silhouette score) is for evaluating clustering. Option E (histogram of target) is univariate and does not show feature-target relationships.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Generate a confusion matrix for the target variable.
Why it's wrong here
Confusion matrix is used for classification tasks, not for regression with continuous target.
- ✗
Compute the silhouette score for each feature.
Why it's wrong here
Silhouette score is used to evaluate clustering, not for understanding relationships with a continuous target.
- ✓
Create scatter plots of numerical features against the target variable.
Why this is correct
Correct. Scatter plots reveal relationships between numerical features and a continuous target.
- ✓
Use box plots to compare target distribution across categorical feature categories.
Why this is correct
Correct. Box plots show distribution of the continuous target across different categories of a categorical feature.
- ✗
Plot a histogram of the target variable.
Why it's wrong here
Histogram of target variable is univariate and does not show relationships with features.
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