The L1 norm, also known as the Manhattan norm or taxicab norm, is a measure of vector magnitude calculated as the sum of the absolute values of its components. It is widely used in optimization problems and machine learning for its ability to promote sparsity in solutions, making it a popular choice in regularization techniques like Lasso regression.

L1 Norm

L1 norm

Manhattan norm

taxicab norm

measure of vector magnitude

sum of the absolute values

Optimization problems involve finding the best solution from a set of feasible solutions, often under given constraints. They are fundamental in various fields such as operations research, economics, and computer science, where the goal is to maximize or minimize an objective function.

optimization problems

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on a task through experience. It leverages data to train models that can make predictions or decisions without being explicitly programmed for specific tasks.

machine learning

promote sparsity

Regularization techniques are methods used in machine learning to prevent overfitting by adding a penalty for larger coefficients in the model. These techniques help improve the generalization of the model on unseen data by controlling the complexity of the model parameters.

regularization techniques

Lasso regression is a linear regression technique that uses L1 regularization to penalize the absolute size of the coefficients, effectively shrinking some of them to zero, which enables feature selection and promotes sparsity in the model. This helps in preventing overfitting and improving the interpretability of the model by identifying the most significant predictors.

CUSTOMIZE YOUR LEARNING

CUSTOMIZE YOUR LEARNING

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