MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA on a dataset with 1,000 features and 10,000 rows. The target is binary. The scientist wants to reduce dimensionality while preserving information related to the target. Which TWO methods are appropriate?
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
✓
L1-regularized logistic regression
Options C and D are correct. L1-regularized logistic regression (option C) drives coefficients to zero for irrelevant features, effectively performing feature selection. Mutual information-based feature selection (option D) measures dependency between each feature and the target, selecting features with highest mutual information. Option A (PCA) is unsupervised and may discard target-related variance. Option B (Autoencoders) is unsupervised and not directly target-aware. Option E (t-SNE) is for visualization, not feature selection.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
Unsupervised; may lose target information.
- ✗
Autoencoders
Why it's wrong here
Unsupervised; not directly target-aware.
- ✓
L1-regularized logistic regression
Why this is correct
Can perform feature selection by shrinking coefficients to zero.
- ✓
Mutual information-based feature selection
Why this is correct
Selects features with high dependency on target.
- ✗
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Why it's wrong here
For visualization, not feature selection.
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