data_analysis:shrinkage_methods

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data_analysis:shrinkage_methods [2020/04/09 07:00] – [best subset 문제] prgramdata_analysis:shrinkage_methods [2025/07/07 14:12] (current) – external edit 127.0.0.1
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 ====== shrinkage methods ====== ====== shrinkage methods ======
 +[[blog:easy_lasso_logistic_regression]]
 [[https://www.stat.cmu.edu/~ryantibs/papers/bestsubset.pdf|best subset vs LASSO]] [[https://www.stat.cmu.edu/~ryantibs/papers/bestsubset.pdf|best subset vs LASSO]]
  
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 ( $ \lambda $ 에 대해 s가 존재, 1-1 correspondence ) ( $ \lambda $ 에 대해 s가 존재, 1-1 correspondence )
 => ridge와 lasso 는 best subset의 풀기 힘든 문제를, 풀기 쉬운 문제로 대체한 계산 가능한 대안. => ridge와 lasso 는 best subset의 풀기 힘든 문제를, 풀기 쉬운 문제로 대체한 계산 가능한 대안.
 +
 +{{:data_analysis:pasted:20200409-171304.png}}
 +그림 출처 : ESL 
  
 {{:data_analysis:pasted:20200403-221625.png?500}} {{:data_analysis:pasted:20200403-221625.png?500}}
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 Cross Validation Cross Validation
  
 +
 +※ elestic net penalty
 +$ \lambda \sum (\alpha \beta_j^2 + (1-\alpha)|\beta_j|) $
  
  
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