Mahalanobis Distance

Mahalanobis Distance is a statistical measure used to determine the similarity between two datasets. It takes into account the correlation between variables and the scales of the variables. It is often used in multivariate analysis, pattern recognition, and outlier detection. The Mahalanobis distance between two points is the distance between them after adjusting for the covariance between variables. It is calculated by taking the square root of the sum of squared differences between the two points, where each difference is divided by the corresponding variance-covariance matrix. The Mahalanobis distance is a useful tool for identifying outliers in a dataset, as points that are far from the center of the dataset are likely to have a high Mahalanobis distance.

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Mahalanobis Distance

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