Member-only story
DATA SCIENCE THEORY | DISTANCE METRICS | MACHINE LEARNING
Distance Metrics in Machine Learning
Euclidean, Manhattan, Minkowski, Cosine, Levenshtein and more — A walkthrough of the most widely used ones
To read this article for free click here.
Distance metrics might sound like an abstract technical concept but they lie at the heart of many machine learning processes. They govern how an algorithm decides whether two pieces of data are close or far apart in terms of similarity. From detecting fraud in credit card transactions to grouping news articles by topic, distance metrics quietly power clustering, classification, and anomaly detection. Yet because they often operate behind the scenes, many people new to data analysis overlook just how important they are.
To understand what distance metrics really are, imagine you want to seat friends at a large dinner party in a way that sparks great conversation. You know which people share hobbies or interests, which ones prefer sports or music, and who loves to cook or paint. You then try to group them so that those with common ground end up at the same table. In essence, you are measuring how ‘far apart or ‘close’ two friends are, not by literal physical distance but by their personal…
