MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset with categorical variables. Which TWO EDA techniques are appropriate for understanding the relationship between a categorical feature and a continuous target? (Choose TWO.)
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
✓
Violin plots
Box plots (E) and violin plots (B) are both effective for visualizing the distribution of a continuous variable across different categories. Violin plots combine box plots with kernel density estimation, providing a richer view. Correlation matrix (A) is for numerical variables. Scatter plots (C) require two continuous variables. Bar chart of counts (D) shows frequency distribution of categories, not relationship with a continuous target.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Correlation matrix
Why it's wrong here
Correlation matrix is for numerical features only.
- ✓
Violin plots
Why this is correct
Violin plots show density and distribution across categories.
- ✗
Scatter plot with categorical variable on x-axis
Why it's wrong here
Scatter plots are not suitable for categorical x-axis; jittering may help but less direct than box plot.
- ✗
Bar chart of category counts
Why it's wrong here
Bar chart shows frequency, not relationship with target.
- ✓
Side-by-side box plots
Why this is correct
Box plots show distribution of continuous variable for each category.
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