Cumulative explained variance refers to the total amount of variance that is accounted for by a subset of principal components in data analysis, especially in Principal Component Analysis (PCA). This metric helps to understand how many components are needed to explain a significant portion of the variability in the dataset, guiding decisions about dimensionality reduction while preserving important information.
congrats on reading the definition of Cumulative Explained Variance. now let's actually learn it.