juniorUnsupervised Learning
What is PCA in unsupervised learning?
Updated May 15, 2026
Short answer
PCA reduces dimensionality by projecting data into principal components.
Deep explanation
It identifies directions of maximum variance using eigenvectors of covariance matrix.
Real-world example
Reducing image dimensions for faster processing.
Common mistakes
- Assuming PCA preserves interpretability of features.
Follow-up questions
- What are eigenvalues in PCA?
- When should PCA be used?