MLS-C01 Exploratory Data Analysis Practice Question
A team has a dataset with 500 features and wants to reduce dimensionality. During EDA, they compute the variance of each feature. Which finding would most likely lead to feature removal?
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
✓
Some features have near-zero variance
Features with near-zero variance have little to no information content and are often redundant for modeling. Removing them reduces dimensionality without significant loss. Option A is incorrect: high correlation between features suggests multicollinearity, but variance is not the direct measure; correlation is addressed by other techniques like PCA. Option B is incorrect: negative covariance with the target indicates an inverse relationship, which can be informative. Option C is incorrect: high variance often indicates useful information, though it may warrant scaling; it is not a reason for removal.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Some features have high correlation with each other
Why it's wrong here
High correlation between features suggests multicollinearity, but this is not directly about variance. Removing features based on variance alone would not target this issue, so this finding is not a reason for removal based on variance.
- ✗
Some features have negative covariance with the target
Why it's wrong here
Negative covariance with the target indicates an inverse relationship, which can be informative. This is not a reason to remove the feature based on variance.
- ✗
Some features have very high variance
Why it's wrong here
High variance often indicates that the feature contains useful variation and information. It is not a reason for removal; in fact, high variance features may be important.
- ✓
Some features have near-zero variance
Why this is correct
Near-zero variance means the feature has very little variation across samples, providing almost no discriminative power. Removing such features reduces dimensionality without significant loss of information.
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