MLS-C01 Exploratory Data Analysis Practice Question
A data analyst is performing exploratory data analysis on a dataset with 100 features. The analyst wants to identify which features contribute most to the variance in the data. Which technique should the analyst use?
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
✓
Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is the correct technique because it is an unsupervised dimensionality reduction method that identifies the principal components, which are linear combinations of the original features that capture the maximum variance in the data. Option A (K-means) is incorrect because it is a clustering algorithm that groups data points, not used for analyzing feature variance. Option C (t-SNE) is incorrect because it is primarily used for visualizing high-dimensional data in lower dimensions but does not provide explicit variance contributions. Option D (LDA) is incorrect because it is a supervised method that requires class labels and aims to maximize class separability, not variance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
K-means clustering
Why it's wrong here
K-means is used for clustering, not variance analysis.
- ✓
Principal Component Analysis (PCA)
Why this is correct
PCA decomposes the data into components that capture the maximum variance.
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t-Distributed Stochastic Neighbor Embedding (t-SNE)
Why it's wrong here
t-SNE is for visualization of high-dimensional data, not for variance decomposition.
- ✗
Linear Discriminant Analysis (LDA)
Why it's wrong here
LDA is a supervised technique that maximizes class separability.
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