MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset with a large number of features. The scientist suspects that some features are redundant because they are highly correlated with each other. Which technique should the scientist use during EDA to identify and remove such redundant features?
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
✓
Variance Inflation Factor (VIF)
Variance Inflation Factor (VIF) quantifies multicollinearity by measuring how much the variance of a coefficient is inflated due to correlation with other features. Features with high VIF (typically >5 or >10) are considered highly correlated and can be removed. Option A is incorrect because chi-square test is used for testing independence between categorical variables, not for identifying redundant features. Option B is incorrect because PCA reduces dimensionality by creating new uncorrelated features, but it does not directly identify which original features are redundant. Option C is incorrect because while a correlation matrix heatmap can show pairwise correlations, it does not account for multicollinearity among multiple features; VIF is more comprehensive.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Chi-square test
Why it's wrong here
Chi-square test is for independence between categorical variables.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA reduces dimensionality but does not identify which original features are redundant.
- ✗
Correlation matrix heatmap
Why it's wrong here
A correlation matrix shows pairwise correlations but does not quantify the combined effect of multiple features.
- ✓
Variance Inflation Factor (VIF)
Why this is correct
VIF measures how much the variance of a regression coefficient is inflated due to multicollinearity.
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