Manhattan Distance
Manhattan Distance is a distance metric used in Data Science to measure the distance between two points in a grid-like system. It is also known as Taxicab Distance or L1 Distance. The Manhattan Distance between two points is the sum of the absolute differences of their coordinates. In other words, it is the distance a taxi would have to travel on a grid-like street system to reach the destination, where the taxi can only move horizontally or vertically. The Manhattan Distance is often used in machine learning algorithms such as k-nearest neighbors (KNN) and clustering algorithms. It is also used in computer vision to compare images and detect similarities based on their pixel values.
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