MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with many features and wants to identify which features are most correlated with the target variable. Which EDA technique should be used?
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
✓
Correlation matrix
A correlation matrix displays pairwise Pearson correlation coefficients between all numeric features and the target variable, enabling quick identification of the most correlated features. Option A (box plots grouped by target) is useful for visualizing feature distributions across target categories but does not directly measure correlation strength. Option B (scatter plot matrix) can show pairwise relationships but becomes impractical with many features and lacks a single quantitative correlation measure. Option C (histogram) only shows the distribution of a single feature, not its relationship with the target. Therefore, the correlation matrix is the appropriate EDA technique for identifying features most correlated with the 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.
- ✗
Box plots grouped by target
Why it's wrong here
Box plots show distributions by category, not correlation.
- ✗
Scatter plot matrix
Why it's wrong here
Scatter plot matrix is useful but not the most efficient for many features.
- ✗
Histogram of each feature
Why it's wrong here
Histograms show distribution, not correlation.
- ✓
Correlation matrix
Why this is correct
Correlation matrix provides a compact view of pairwise correlations.
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