MLS-C01 Exploratory Data Analysis Practice Question
A machine learning engineer is analyzing a dataset with 500 features and suspects multicollinearity. Which TWO techniques can help identify and address multicollinearity during exploratory data analysis? (Choose TWO.)
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 Variance Inflation Factor (VIF) for each feature
Variance Inflation Factor (VIF) measures how much the variance of a regression coefficient is inflated due to multicollinearity. Correlation matrix heatmap shows pairwise correlations. PCA reduces dimensionality but does not directly identify multicollinearity. Lasso regression addresses it via regularization but is a modeling step. t-SNE is for visualization of high-dimensional data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply t-SNE for visualization
Why it's wrong here
t-SNE is for visualization, not for detecting multicollinearity.
- ✗
Apply Principal Component Analysis (PCA)
Why it's wrong here
PCA transforms features but does not identify multicollinearity.
- ✓
Calculate Variance Inflation Factor (VIF) for each feature
Why this is correct
VIF > 5-10 indicates multicollinearity.
- ✓
Generate a correlation matrix heatmap
Why this is correct
High pairwise correlations indicate multicollinearity.
- ✗
Use Lasso regression to select features
Why it's wrong here
Lasso addresses multicollinearity but is a modeling technique, not EDA.
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Same concept, more angles
1 more way this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A machine learning team is analyzing a dataset with numerical features. They compute the pairwise correlation matrix and find that two features, 'X1' and 'X2', have a correlation coefficient of 0.98. The team plans to train a linear regression model. Which of the following actions should the team take to avoid multicollinearity issues?
easy- A.Perform PCA on the dataset to reduce dimensionality.
- B.Add an interaction term between X1 and X2 to the model.
- C.Standardize both features using Z-score normalization.
- ✓ D.Remove one of the two highly correlated features.
Why D: Removing one of the highly correlated features reduces multicollinearity. Option A is wrong because PCA creates new uncorrelated features but is not necessary for just two correlated features. Option B is wrong because adding an interaction term between X1 and X2 would actually increase multicollinearity. Option C is wrong because standard scaling does not address correlation between features.
JA
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