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What’s your understanding of PCA and its inherent disadvantages?

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Anonymous

8 months ago
3.3Strong
PCA, or principal component analysis, is a process to determine what are the most important features. I like to think about it in terms of eigenvalues. If you have a diagonal matrix, then the eigenvalues are the diagonal terms, and the eigenvectors are the basis vectors. In PCA, the principal components and their effects aren't as visible, but it is a process to determine them. If I recall correctly, PCA will return the absolute value of the eigenvalues, rather than the eigenvalue itself. 

  • Can you break down the principles of PCA and its drawbacks in practice?
  • Can you outline the concept of PCA and its potential pitfalls?
  • Could you elucidate Principal Component Analysis and discuss its limitations?
  • Could you provide a brief explanation of PCA and its limitations?
  • Explain Principal Component Analysis. What are its disadvantages?
  • How can you explain PCA and where does it fall short?
  • How would you describe PCA and what drawbacks might it have?
  • In what way does PCA work and what are some of its cons?
  • What do you perceive to be the main uses and disadvantages of PCA?
  • What’s your understanding of PCA and its inherent disadvantages?

Interview question asked to Data Scientists and Machine Learning Engineers interviewing at LogMeIn, Motorola Solutions, Zendesk and others: What’s your understanding of PCA and its inherent disadvantages?.