MLS-C01 Exploratory Data Analysis Practice Question
Which THREE are valid reasons to perform feature scaling during exploratory data analysis?
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
✓
To improve performance of distance-based algorithms like KNN.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
To improve performance of distance-based algorithms like KNN.
Why this is correct
Distance algorithms are sensitive to scale.
- ✗
To change the shape of the feature distribution.
Why it's wrong here
Scaling does not change distribution shape.
- ✗
To increase the number of features.
Why it's wrong here
Scaling does not increase number of features.
- ✓
To ensure features have zero mean and unit variance.
Why this is correct
Standardization centers and scales data.
- ✓
To reduce the effect of outliers by clipping values.
Why this is correct
Scaling can include robust methods that handle outliers.
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