Mahalanobis Distance

Mahalanobis Distance is a way to measure how similar or different two points are in a dataset, considering the overall pattern of data variation. Unlike regular distance measures like Euclidean distance, it accounts for the spread and correlation of the data, making it more accurate for identifying outliers or similarities when data features are related. Think of it as measuring how far apart two points are, but with an understanding of the shape and orientation of the data distribution, providing a more meaningful comparison in complex datasets.