About this document
Regularization in Applied Machine Learning by betifi is a document available to read on EtoBox.
This document discusses regularization techniques for regression models. It introduces regularization as a way to perform constrained optimization and data-driven variable selection when there are many weakly correlated variables. Ridge regression and lasso regression are presented as common regularization methods that minimize coefficients subject to a constraint. Cross-validation is discussed as a way to choose hyperparameters and compare model performance.
- Author
- betifi
- Language
- EN