MLS-C01 Exploratory Data Analysis Practice Question
A data scientist has a dataset with 500 features and wants to reduce dimensionality for visualization. Which technique is most appropriate for identifying the two components that capture the most variance?
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 a linear dimensionality reduction technique that finds the directions (principal components) of maximum variance in the data, making it ideal for identifying the two components that capture the most variance for visualization. Option A is incorrect because t-SNE is a non-linear technique focused on preserving local neighborhood structure, not on maximizing global variance. Option B is incorrect because Linear Discriminant Analysis (LDA) is a supervised method that requires class labels to separate classes, not to maximize variance. Option D is incorrect because K-means clustering is an unsupervised algorithm for grouping data points, not a dimensionality reduction method.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Why it's wrong here
t-SNE is for visualization but does not provide variance-based components.
- ✗
Linear Discriminant Analysis (LDA)
Why it's wrong here
LDA is supervised and requires class labels.
- ✓
Principal Component Analysis (PCA)
Why this is correct
PCA projects data onto directions of maximum variance.
- ✗
K-means clustering
Why it's wrong here
K-means is a clustering algorithm, not dimensionality reduction.
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