How does PCA decide the optimal number of components?

PCA selects components based on explained variance threshold or eigenvalue distribution.

Choosing the number of principal components involves balancing dimensionality reduction and information retention. Common strategies include cumulative explained variance (e.g., 95%), elbow method on scree plot, or eigenvalue > 1 rule (Kaiser criterion). Each method approximates how much variance is retained while reducing noise and redundancy.