S5E09 Regularized Variable Selection Methods
Quantitude
Regularization in Variable Selection Methods
This chapter discusses the concept of regularization in variable selection methods, comparing it to traditional stepwise regression. It explores different forms of regularization, such as ridge regression, lasso, and elastic net, explaining the role of lambda as a tuning parameter. The advantages of regularization methods, such as enhanced replicability and robustness to overfitting, are highlighted.
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