Eigenvectors, Eigenvalue, Data Science, Machine Learning, Principal Component.
covariance, otherwise there is a negative effect. This helps explain why the features are zero-centered (mean of 0) given that what we really care about is positive vs negative relationships. From this we get the symmetric covariance matrix: Covariance matrix: variance on diagonals, pairwise