L1 Norm

L1 Norm is a mathematical concept used in Data Science and Artificial Intelligence to measure the magnitude of a vector in a space. It is also known as Manhattan distance or Taxicab norm. L1 Norm is calculated by taking the absolute values of the vector components and summing them up. In other words, it is the sum of the absolute differences between the coordinates of the vector. L1 Norm is commonly used in machine learning algorithms such as Lasso Regression, where it is used as a penalty term to reduce the complexity of the model and prevent overfitting. It is also used in clustering algorithms such as K-Means, where it is used to measure the distance between data points and cluster centroids.
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