Contextual Distances: The Mahalanobis Metric Explained

A deep-dive into how Mahalanobis distance measures distance not just by coordinates but within the data's own landscape, using the covariance structure to account for correlations. We trace its origin from skull measurements to modern uses in clustering, anomaly detection, and fraud detection, and unpack the formula, intuition, and practical caveats—like the multivariate normal assumption and sensitivity to outliers. A practical guide to when this metric shines and when alternatives may be wiser.

Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information.

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