Distance Metric
In data science, a distance metric is a function that measures the distance between two points in a dataset. It is used to determine the similarity or dissimilarity between two data points. Distance metrics are commonly used in clustering, classification, and anomaly detection algorithms. The most commonly used distance metrics are Euclidean distance, Manhattan distance, and Cosine similarity. Euclidean distance is the straight-line distance between two points in a dataset. Manhattan distance is the sum of the absolute differences between the coordinates of two points. Cosine similarity measures the cosine of the angle between two vectors in a high-dimensional space. Other distance metrics include Minkowski distance, Mahalanobis distance, and Hamming distance.
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