MLS-C01 Exploratory Data Analysis Practice Question
A machine learning engineer is exploring a dataset with 50 features. Some features are highly correlated. Which technique should the engineer use to reduce dimensionality while preserving 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
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Principal Component Analysis (PCA)
PCA (Principal Component Analysis) is the standard technique for dimensionality reduction by projecting data onto principal components that capture maximum variance. LDA is supervised and aims to separate classes. t-SNE is for visualization. Autoencoders can reduce dimensionality but are more complex. Factor analysis assumes latent factors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Principal Component Analysis (PCA)
Why this is correct
PCA reduces dimensionality by finding components that maximize variance.
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Factor Analysis
Why it's wrong here
Factor Analysis assumes underlying latent factors, but PCA is more direct for variance preservation.
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t-Distributed Stochastic Neighbor Embedding (t-SNE)
Why it's wrong here
t-SNE is primarily for visualization, not for preserving global variance.
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Linear Discriminant Analysis (LDA)
Why it's wrong here
LDA is supervised and maximizes class separability, not variance.
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