Bird
0
0

What is the main purpose of Singular Value Decomposition (SVD) in data science?

easy📝 Conceptual Q1 of 15
SciPy - Linear Algebra (scipy.linalg)
What is the main purpose of Singular Value Decomposition (SVD) in data science?
ATo remove missing values from data
BTo factorize a matrix into three simpler matrices
CTo calculate the mean of a dataset
DTo sort data in ascending order
Step-by-Step Solution
Solution:

  1. Step 1: Understand SVD concept

    SVD breaks a matrix into three matrices: U, S, and VT, simplifying complex data.

  2. Step 2: Identify the purpose in data science

    This factorization helps in dimensionality reduction, noise reduction, and data compression.

  3. Final Answer:

    To factorize a matrix into three simpler matrices -> Option B

  4. Quick Check:

    SVD purpose = Factorization [OK]
Quick Trick: SVD breaks matrices into U, S, VT for easier analysis [OK]
Common Mistakes:
MISTAKES
  • Thinking SVD sorts data
  • Confusing SVD with mean calculation
  • Assuming SVD cleans data

Want More Practice?

15+ quiz questions · All difficulty levels · Free

Free Signup - Practice All Questions
More SciPy Quizzes
Constants and Special Functions - Error function (erf) - Quiz 15hard Linear Algebra (scipy.linalg) - Matrix inverse (inv) - Quiz 3easy Linear Algebra (scipy.linalg) - Solving linear systems (solve) - Quiz 6medium Linear Algebra (scipy.linalg) - Cholesky decomposition - Quiz 7medium SciPy Basics and Scientific Computing - Importing SciPy submodules - Quiz 3easy Sparse Matrices (scipy.sparse) - Sparse matrix operations - Quiz 1easy Sparse Matrices (scipy.sparse) - COO format (Coordinate) - Quiz 7medium Sparse Matrices (scipy.sparse) - CSC format (Compressed Sparse Column) - Quiz 7medium Statistical Functions (scipy.stats) Basics - Percentiles and quantiles - Quiz 9hard Statistical Tests - Kolmogorov-Smirnov test - Quiz 9hard