AI engineering glossary · Classical ML algorithms
What is explained variance ratio?
Explained variance ratio: The fraction of a dataset's total variation captured by each principal component, so the sum over kept components shows how much information survives. It is used to decide how many components to retain.
Explained variance ratio: an example
Components explain 0.55, 0.25, 0.10 and 0.05 of the variance, so the first three keep 90%.
Related terms
More Classical ML algorithms terms
- Extremely randomised trees
- Feature importance
- Gaussian mixture model
- Gaussian naive Bayes
- Gaussian process
- Gini impurity
- Gradient boosting
- HDBSCAN
Where you learn it
Explained variance ratio is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.