Penalized Regression (Lasso and Ridge)  Python for Integrated Circuits   An Online Book  

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  
================================================================================= Penalized regression is a general term that refers to the practice of adding a penalty term to the standard linear regression objective function. This penalty term is used to discourage large coefficients in the model. It's a broad concept that encompasses various types of regularization, including Lasso and Ridge. Elastic Net is a penalized regression technique. It combines the L1 regularization (Lasso) and L2 regularization (Ridge) techniques into a single model, providing a compromise between the two. Here's why Elastic Net is considered a penalized regression technique:
By combining both L1 and L2 regularization terms, Elastic Net offers a flexible way to control the biasvariance tradeoff in regression modeling. The L1 penalty helps with feature selection and sparsity, while the L2 penalty helps with coefficient shrinkage and multicollinearity control. The mixing parameter in Elastic Net allows you to adjust the balance between these two penalties, providing a versatile tool for regression tasks, especially when dealing with highdimensional datasets with potentially correlated features. 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. ============================================


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