hardMultiple SelectObjective-mapped
MLA-C01 Practice Question: A machine learning team is building a product…
A machine learning team is building a product recommendation system. They have a dataset with millions of users and thousands of products. The team wants to reduce the dimensionality of the user-product interaction matrix while preserving as much variance as possible. Which THREE techniques are appropriate for dimensionality reduction? (Choose THREE.)
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)
PCA, SVD, and t-SNE are common dimensionality reduction techniques. PCA and SVD are linear methods that maximize variance. t-SNE is non-linear and good for visualization. Lasso is for feature selection, not matrix factorization. Mutual information is for feature selection, not reduction.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Lasso regularization
Why it's wrong here
Lasso is a regularization technique for feature selection in models, not a dimensionality reduction method for matrices.
- ✗
Mutual information feature selection
Why it's wrong here
Mutual information scores features but does not perform reduction by transforming the feature space.
- ✓
Principal Component Analysis (PCA)
Why this is correct
PCA reduces dimensions by projecting onto principal components that capture maximum variance.
- ✓
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Why this is correct
t-SNE reduces high-dimensional data to 2 or 3 dimensions for visualization, preserving local structure.
- ✓
Singular Value Decomposition (SVD)
Why this is correct
SVD factorizes the matrix into lower-rank approximations, commonly used in collaborative filtering.
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