The Mahalanobis Distance

The Mahalanobis Distance measures how far a point is from a set of data, considering the relationships and spread within that data. Unlike straight-line distance, it accounts for correlations between variables, so it recognizes when some differences are more significant due to how data points vary together. For example, if two features are closely linked, a small change in one might be more meaningful than the same change in an unrelated feature. This distance helps in identifying outliers and classifying data more accurately because it adjusts for the data's overall structure, providing a more informed measure of similarity.