MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing exploratory data analysis on a dataset with 100 features. They want to identify which features are most correlated with the target variable. Which THREE methods are appropriate for this task?
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
✓
Pearson correlation coefficient
Pearson correlation coefficient measures linear relationship between features and target. Feature importance from a random forest provides a ranking of feature relevance. Mutual information captures both linear and non-linear dependencies. Together, these three methods effectively identify correlated features. Variance threshold is used for removing low-variance features, not for correlation. One-hot encoding is a preprocessing technique for categorical variables, not a correlation method.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Pearson correlation coefficient
Why this is correct
Measures linear correlation between each feature and the target.
- ✗
Variance threshold
Why it's wrong here
Variance threshold removes low-variance features but does not assess correlation with target.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding is a preprocessing step for categorical variables, not a correlation analysis.
- ✓
Feature importance from a random forest
Why this is correct
Tree-based models provide importance scores based on how much each feature reduces impurity.
- ✓
Mutual information
Why this is correct
Mutual information quantifies the dependency between each feature and the target variable by measuring the reduction in uncertainty of the target given the feature, capturing both linear and non-linear relationships. This satisfies the stem’s requirement to identify the most correlated features among 100, as it does not assume a specific functional form, unlike Pearson correlation which only detects linear dependence.
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