MLS-C01 Exploratory Data Analysis Practice Question
During EDA, a data scientist plots the distribution of a numeric feature and observes that it is right-skewed. The feature will be used as input to a linear model. Which transformation should the data scientist apply?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that standardization or scaling fixes skewness, but candidates must remember that only shape-altering transformations like log or Box-Cox address non-normality, not just rescaling.
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
✓
Log transformation
A right-skewed distribution indicates that the feature has a long tail on the right, which can violate the linear model assumption of normally distributed errors. The log transformation compresses the high values and expands the low values, making the distribution more symmetric and stabilizing variance, which improves linear model performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Square transformation
Why it's wrong here
Square transformation increases right skewness.
- ✓
Log transformation
Why this is correct
Log transformation compresses the tail and reduces right skewness.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding is for categorical features, not numeric.
- ✗
Standardization (Z-score)
Why it's wrong here
Standardization centers and scales but does not change skewness.
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