L1 Regularization versus L1 Loss (absolute loss or mean absolute error (MAE)) - Python for Integrated Circuits - - An Online Book - |
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Python for Integrated Circuits http://www.globalsino.com/ICs/ | |||||||||||||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | |||||||||||||||||||
================================================================================= L1 Regularization and L1 Loss are two distinct concepts in machine learning, although they both involve the use of the L1 norm. Lasso stands for "Least Absolute Shrinkage and Selection Operator." It is a specific type of penalized regression that uses L1 regularization. Lasso adds a penalty term to the linear regression objective function that is proportional to the absolute values of the model coefficients. This encourages sparsity in the model, meaning it can set some coefficients to exactly zero, effectively performing feature selection. Table 3879a lists the key differences between the two regularization. Table 3879a. Key differences between the two regularization.
Note that penalized regression, Lasso, and Ridge regression are related concepts in the field of linear regression, but they are not exactly the same as L1 and L2 regularization. Table 3879b. Applications and related concepts of L1 regularization.
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