MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset and finds that the variance of a feature is 0. What should be done with this feature?
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
✓
Remove the feature from the dataset
When a feature has zero variance, it means all values are identical (constant). Such a feature provides no discriminative information for machine learning models and can be safely removed. Removing it reduces dimensionality without losing any information. Option A is correct. Option B (creating interaction terms) is incorrect because interacting a constant with any feature yields a constant. Option C (min-max scaling) does not change the fact that the feature is constant; it remains constant after scaling. Option D (imputing missing values) is irrelevant since zero variance does not imply missing values.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Remove the feature from the dataset
Why this is correct
Constant feature provides no predictive power.
- ✗
Create interaction terms with other features
Why it's wrong here
Interaction with constant feature remains constant.
- ✗
Apply Min-Max scaling to normalize the feature
Why it's wrong here
Scaling a constant feature yields all same values, still useless.
- ✗
Impute missing values using the mean
Why it's wrong here
Imputation is not relevant; feature has no missing values.
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