MLS-C01 Exploratory Data Analysis Practice Question
During EDA, a data scientist creates a scatter matrix of numerical features and notices that some features have a funnel-shaped pattern (variance increases with the mean). What is the appropriate transformation to stabilize variance?
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between transformations that stabilize variance (log, Box-Cox) versus those that only standardize (Z-scores) or are domain-specific (sine), and candidates may incorrectly choose Box-Cox with lambda=0 thinking it is a separate technique, missing that the log transformation is the canonical answer for funnel-shaped heteroscedasticity.
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
✓
Apply log transformation.
A funnel-shaped pattern in a scatter matrix indicates heteroscedasticity, where variance increases with the mean. The log transformation is appropriate because it compresses the scale of the data, making the variance more constant across the range of values, which stabilizes variance for right-skewed or multiplicative 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 log transformation.
Why this is correct
Log transformation stabilizes variance when variance increases with mean.
- ✗
Standardize the features using Z-scores.
Why it's wrong here
Standardization does not fix heteroscedasticity.
- ✗
Apply a sine transformation.
Why it's wrong here
Sine transformation is not for variance stabilization.
- ✗
Apply Box-Cox transformation with lambda=0.
Why it's wrong here
A Box-Cox transformation with lambda=0 applies a log transform, which stabilises variance when the standard deviation is proportional to the mean (constant coefficient of variation). The funnel-shaped pattern described indicates variance increasing with the mean, but the relationship may not be multiplicative; the log transform overcorrects if the variance grows quadratically or linearly with the mean. It is tempting because the log transform is a common variance-stabilising technique for right-skewed data, and would be correct if the scatter matrix showed a constant coefficient of variation across feature values.
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