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A covariance matrix is a square matrix that shows the covariance between every pair of variables in a given data set, where each element in the matrix represents the corresponding covariance.
Mahalanobis distance is a distance metric that finds the distance between a point and a distribution. It’s often used for detecting outliers in multivariate data.