l1-norm (2) 썸네일형 리스트형 [R] Variable Selection Methods : Lasso 1. Lasso Regression Ridge have disadvantages of including all p predictors in the final model. What we want to do is variable selection. Lasso shrinks \(\hat{\beta}\) towards zero. \(RSS + \lambda\sum_{j=1}^{p}|\beta_j|\) The \(l_1\)-norm of \(\hat{\beta}\) : \(df(\hat{\beta}_{\lambda_1}) = 0 [R] Variable Selection Methods : Ridge 1. Variable Selection Methods We cannot use subset selection model in \(n > Var(\hat{\beta}^{sh})\) Examples Ridge Lasso Elastic Net : Ridge + Lasso 3. Ridge Regression \(RSS + \lambda\sum_{j=1}^{p}\beta_j^2\) where \(\lambda >= 0\) is a tuning parameter. For a grid of \(\lambda\) : \(\lambda_{max} = \lambda_1 > ... > \lambda_m = \lambda_{min}\). The \(l_2\)-norm of \(\hat{\beta}\) : \(||\hat{\b.. 이전 1 다음
