MLS-C01 Exploratory Data Analysis Practice Question
A machine learning engineer is analyzing a dataset with a large number of features (p >> n). The engineer suspects that many features are irrelevant. Which THREE methods are suitable for feature selection during exploratory data analysis? (Choose THREE.)
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 features with variance below a threshold (e.g., <0.01)
In a high-dimensional dataset (p >> n), feature selection is crucial. Option B (Variance Threshold) is suitable because features with low variance (e.g., <0.01) are likely to be constant or near-constant and thus uninformative. Option C (removing features with high pairwise correlation >0.95) helps reduce redundancy and multicollinearity. Option D (mutual information) is a filter method that measures dependency between each feature and the target, allowing selection of the most relevant features. Option A (Lasso regression) is a modeling method that can be used for feature selection but is not typically used during EDA; it is a supervised learning technique. Option E (PCA) is a dimensionality reduction technique that creates new components, not feature selection. Therefore, the correct answers are B, C, D.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fit a Lasso regression model and select features with non-zero coefficients
Why it's wrong here
Lasso is a modeling step, not exploratory; it may not be suitable for all data types.
- ✓
Remove features with variance below a threshold (e.g., <0.01)
Why this is correct
Low-variance features provide little information and can be removed.
- ✓
Remove features with high pairwise correlation (e.g., >0.95)
Why this is correct
High correlation indicates redundancy; removing them reduces multicollinearity.
- ✓
Calculate mutual information between each feature and the target, and keep top k features
Why this is correct
Mutual information captures non-linear dependencies and is useful for feature selection.
- ✗
Apply Principal Component Analysis (PCA) and select top components
Why it's wrong here
PCA creates new features, it does not select original features.
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