MLS-C01 Exploratory Data Analysis Practice Question
During exploratory data analysis, a data scientist discovers that a feature has a variance of 0.01, while other features have variances around 1.0. Which action should be taken?
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
✓
Consider removing the feature or applying variance threshold.
A feature with near-zero variance (0.01) compared to others (~1.0) likely has little predictive power and can cause numerical instability in models. Variance thresholding is a standard preprocessing step to remove low-variance features. Option A is wrong: scaling to unit variance does not address the fundamental issue of low information content; it merely changes the scale. Option B is wrong: log transformation changes distribution shape but does not meaningfully increase variance; the variance will remain low. Option C is wrong: imputation is for missing values, which is unrelated to low variance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Scale the feature to have unit variance.
Why it's wrong here
Scaling does not add information; the feature may still be useless.
- ✗
Apply a log transformation to the feature.
Why it's wrong here
Log transformation cannot increase variance if the feature is constant.
- ✗
Impute missing values in the feature.
Why it's wrong here
Low variance is not necessarily due to missing values.
- ✓
Consider removing the feature or applying variance threshold.
Why this is correct
Near-zero variance features are often uninformative.
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