midPCA
What is explained variance ratio in PCA?
Updated May 17, 2026
Short answer
It measures how much variance each principal component explains.
Deep explanation
Explained variance ratio is eigenvalue divided by sum of all eigenvalues. It indicates contribution of each component to total variance, helping decide number of components.
Real-world example
Choosing 95% variance retention in ML pipelines.
Common mistakes
- Selecting components without checking variance ratio.
Follow-up questions
- What is good variance threshold?
- Does higher variance mean better features?