MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA on a dataset with 500 features. The dataset has a mix of numeric and categorical features. The scientist wants to identify which features have a strong nonlinear relationship with the target variable. Which technique is most appropriate?
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 between each feature and the target.
Mutual information can capture any kind of dependency (including nonlinear) between features and target. Option A (ANOVA) compares means across groups but assumes linearity. Option B (Pearson correlation) only captures linear relationships. Option D (Chi-squared test) is for categorical features, not suitable for the mix of numeric and categorical features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use ANOVA to compare feature means across target classes.
Why it's wrong here
ANOVA is for comparing means, not for measuring relationship strength.
- ✗
Compute Pearson correlation coefficients.
Why it's wrong here
Pearson correlation only captures linear relationships.
- ✓
Calculate mutual information between each feature and the target.
Why this is correct
Mutual information measures any dependency, including nonlinear.
- ✗
Perform chi-squared tests for each feature.
Why it's wrong here
Chi-squared tests are for categorical variables only.
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