What is Singular Value Decomposition (SVD) used for in machine learning?

Subject: Machine Learning

Topic: Statistical Learning and Feature Engineering

Options:

1. Option A: Classification of data.
2. Option B: Dimensionality reduction, noise reduction, and matrix factorization.
3. Option C: Clustering data points.
4. Option D: Association rule mining.

Correct Answer:

Option B: Dimensionality reduction, noise reduction, and matrix factorization.

Explanation:

SVD is a matrix factorization technique used for dimensionality reduction, noise reduction, data compression, and solving linear systems in machine learning.