neliöMahalanobisetäisyys
neliöMahalanobisetäisyys, often translated as squared Mahalanobis distance, is a statistical measure used to quantify the distance between a point and a distribution, taking into account the correlations between variables. Unlike Euclidean distance, which treats all variables equally and assumes they are independent, squared Mahalanobis distance accounts for the shape and orientation of the data's covariance. It is particularly useful in multivariate analysis for identifying outliers and classifying data points.
The calculation of squared Mahalanobis distance involves the inverse of the covariance matrix of the data.
A key advantage of using the squared Mahalanobis distance is its invariance to linear transformations of the