MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA on a dataset with 1,000 features. The goal is to select the most important features for a regression model. Which technique can be used to rank feature importance quickly?
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
✓
Calculate the correlation coefficient of each feature with the target
Correlation analysis with the target variable is a quick way to rank features. Option B (t-SNE) is used for visualization, not feature ranking. Option C (k-means clustering) is an unsupervised clustering method and does not provide feature importance. Option D (PCA) component loadings show variance contribution but are not a direct ranking of feature importance to the target.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Calculate the correlation coefficient of each feature with the target
Why this is correct
Quick and provides a ranking.
- ✗
Use t-SNE to visualize feature relationships
Why it's wrong here
t-SNE is for visualization, not feature ranking.
- ✗
Run k-means clustering and use cluster centroids
Why it's wrong here
Clustering is unsupervised and not for feature ranking.
- ✗
Apply Principal Component Analysis (PCA) and examine component loadings
Why it's wrong here
PCA is unsupervised and not directly for feature selection.
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