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data are dispersed. (Eigenvectors.) The relative importance of these different directions. (Eigenvalues.) PCA combines our predictors and allows us to drop the eigenvectors that are relatively unimportant The disadvantage of PCA : These components are hard to interpret in prediction Sensitive to