MLS-C01 Modeling Practice Question
A data scientist needs to perform feature scaling for a dataset containing numerical features with different units (e.g., age in years and income in dollars). Which scaling method is most appropriate when the algorithm assumes data is normally distributed?
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
✓
Standardization (Z-score normalization)
Standardization (Z-score normalization) transforms features to have mean 0 and standard deviation 1, which is suitable for algorithms that assume normally distributed data. Option B (Log transformation) is used to reduce skewness, not for scaling when normality is already assumed. Option C (Min-Max scaling) scales to a fixed range (e.g., 0-1) but does not preserve normal distribution properties. Option D (Robust scaling) uses median and IQR, which is robust to outliers but does not assume normality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Standardization (Z-score normalization)
Why this is correct
Standardization centers data around zero with unit variance, suitable for normality assumptions.
- ✗
Log transformation
Why it's wrong here
Log transformation is for reducing skewness, not for scaling features to a standard range.
- ✗
Min-Max scaling
Why it's wrong here
Min-Max scaling does not center data and does not assume normality.
- ✗
Robust scaling (using median and IQR)
Why it's wrong here
Robust scaling is robust to outliers but doesn't assume normality.
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