MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset and finds that two features have a Pearson correlation coefficient of 0.95. Which TWO actions should the data scientist consider? (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
✓
Combine the two features into a single feature using PCA or averaging
A Pearson correlation coefficient of 0.95 indicates strong multicollinearity between the two features. Multicollinearity can inflate coefficient variances and reduce model interpretability. Two standard remedies are to remove one of the correlated features (Option D) or to combine them into a single feature using techniques like PCA, averaging, or summing (Option A). Option B (adding interaction terms) would introduce additional correlated terms and exacerbate multicollinearity. Option C (increasing regularization) can help stabilize coefficients but does not directly address the high pairwise correlation; it is often used as a secondary technique after feature selection or combination. Option E (standard scaling) does not change the correlation coefficient and therefore does not mitigate multicollinearity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Combine the two features into a single feature using PCA or averaging
Why this is correct
Combining captures information from both while reducing dimensionality.
- ✗
Add interaction terms between the features
Why it's wrong here
Interaction terms may increase multicollinearity.
- ✗
Increase regularization strength in the model
Why it's wrong here
Regularization can help with multicollinearity but is not the primary action for correlated features.
- ✓
Remove one of the correlated features
Why this is correct
Removing one reduces redundancy and multicollinearity.
- ✗
Apply standard scaling to both features
Why it's wrong here
Scaling does not change correlation.
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