Chapter 11: The ML Pipeline
Regularization Geometry: How Ridge, LASSO, and Elastic Net Actually Work intermediate
Regularization helps not by fitting the training sample better, but by refusing to trust unstable coefficient estimates — and the SVD of the feature matrix reveals exactly which directions it distrusts and why.
Regularization helps not by fitting the training sample better, but by refusing to trust unstable coefficient estimates — and the SVD of the feature matrix reveals exactly which directions it distrusts and why.
Register to Read
Sign up for a free account to access all 112 primer topics.
Create Free AccountAlready have an account? Sign in
References
Regression Shrinkage and Selection Via the Lasso
Robert Tibshirani
(1996)
— Journal of the Royal Statistical Society: Series B (Methodological)
Regularization and Variable Selection Via the Elastic Net
Hui Zou, Trevor Hastie
(2005)
— Journal of the Royal Statistical Society Series B: Statistical Methodology
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
Trevor Hastie, Robert Tibshirani, Jerome Friedman
(2009)
— Springer-Verlag
Complexity in Factor Pricing Models
Antoine Didisheim, Shikun (Barry) Ke, Bryan T. Kelly, Semyon Malamud
(2023)