MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset of customer transactions. The dataset has 1 million rows and 50 columns. The target variable is a binary flag indicating whether a customer churned. The data scientist runs a correlation matrix on all numerical features and finds that two features have a correlation coefficient of 0.98. Which action should be taken to improve model performance?
⚠ Common exam trap
AWS often tests the misconception that regularization alone fixes multicollinearity, but regularization only penalizes coefficient magnitude, not the linear dependency between 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
✓
Remove one of the two highly correlated features from the dataset.
Two features with a correlation coefficient of 0.98 are nearly perfectly multicollinear. This inflates the variance of coefficient estimates in linear models, making them unstable and reducing interpretability. Removing one of the highly correlated features is a standard dimensionality reduction technique that mitigates multicollinearity without significant information loss, as the remaining feature captures almost the same variance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create an interaction term between the two features.
Why it's wrong here
Interaction terms can increase multicollinearity and complexity.
- ✓
Remove one of the two highly correlated features from the dataset.
Why this is correct
Removing one feature eliminates multicollinearity, simplifying the model and improving interpretability.
- ✗
Increase the regularization parameter (e.g., lambda) in the model.
Why it's wrong here
Regularization helps but does not directly address the redundancy; correlated features can still cause instability.
- ✗
Apply mean-centering to both features to reduce correlation.
Why it's wrong here
Mean-centering does not change the correlation coefficient.
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