Principal components analysis
Turning many correlated features into a few uncorrelated composites, the principal components, ranked by how much of the variance each explains. It reduces dimension at the cost of interpretability.
Where you will meet it
Principal components analysis appears in 2 learning outcomes on the CFA syllabus. A few of them:
- Explain deep, supervised and unsupervised learning and how investment managers use them
“Unsupervised learning has no labels and looks for structure, as in clustering similar stocks or principal components analysis, which condenses many correlated variables into a few factors.”
- Explain unsupervised machine learning algorithms (k-means clustering, principal components analysis and hierarchical clustering) and decide which kinds of problem each handles best
“Principal components analysis replaces many correlated features with a smaller set of uncorrelated composites.”
See also
Practise it
Practise principal components analysis until it is automatic: questions that explain every wrong option.
Take the free diagnostic →