MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with 1 million records and 20 features. The target variable is continuous. The scientist wants to identify non-linear relationships between features and the target. Which technique is MOST suitable for this purpose during exploratory data analysis?
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
✓
Calculate mutual information scores between each feature and the target.
Mutual information captures any kind of dependency, including non-linear. Option A is wrong because a correlation matrix heatmap only shows pairwise linear correlations among features, not feature-target relationships. Option B is wrong because PCA is for dimensionality reduction and does not directly assess feature-target relationships. Option D is wrong because Pearson correlation only measures linear relationships, missing non-linear ones.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Visualize the correlation matrix heatmap of all features.
Why it's wrong here
Heatmap shows feature-feature correlations, not with target.
- ✗
Apply Principal Component Analysis (PCA) and examine the loadings.
Why it's wrong here
PCA is unsupervised; loadings don't relate to target.
- ✓
Calculate mutual information scores between each feature and the target.
Why this is correct
Mutual information captures non-linear dependencies.
- ✗
Compute Pearson correlation coefficients between each feature and the target.
Why it's wrong here
Pearson only detects linear relationships.
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