Distance Metrics
Distance metrics are mathematical functions that measure the dissimilarity or similarity between two data points, typically in a vector space, by quantifying how far apart they are. They are fundamental in fields like machine learning, data mining, and statistics for tasks such as clustering, classification, and nearest neighbor search. Common examples include Euclidean distance, Manhattan distance, and cosine similarity.
Developers should learn distance metrics when working on machine learning algorithms (e.g., k-nearest neighbors, k-means clustering), recommendation systems, or any application requiring similarity or dissimilarity comparisons between data points. They are essential for optimizing performance in tasks like image recognition, natural language processing, and anomaly detection, where accurate distance calculations directly impact model effectiveness.