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  "body": "\n## Summary\n\nThe Mahalanobis distance measures how far a point lies from the centre of a multivariate distribution.\nIts units are adapted to the spread and the correlations of the distribution.\nIt is the [[Euclidean distance|0NPPGHQNXQ]] after the data are transformed so that the [[covariance matrix|0V7G6WMFK0]] becomes the identity.\nIt is used to detect outliers and in [[linear discriminant analysis|ZATS7NV9WD]].\nThe density of a [[multivariate normal distribution|0XN3K7RSZY]] is constant on the sets of constant Mahalanobis distance.\n\n## Explanations\n\n### L1 human\n\nA point that is two units from the mean is unusual in a direction where the data hardly vary, but ordinary in a direction where they vary a lot.\nThe Mahalanobis distance takes this into account by dividing each direction by its own spread.\nIts level sets are ellipses around the mean instead of circles.\nLet the first coordinate have variance $4$ and the second variance $1$, with no correlation.\nThen the points $(2, 0)$ and $(0, 1)$ are both at Mahalanobis distance $1$ from the origin.\n",
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