6.1.1 Best Subset Selection

To perform best subset selection , we fit a separate least squares regression best subset for each possible combination of the p predictors. That is, we fit all p models selection that contain exactly one predictor, all � p 2� = p ( p − 1) / 2 models that contain exactly two predictors, and so forth. We then look at all of the resulting models, with the goal of identifying the one that is best .

The problem of selecting the best model from among the 2 [p] possibilities considered by best subset selection is not trivial. This is usually broken up into two stages, as described in Algorithm 6.1.

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Sub-Chapters (하위 목차)

Algorithm 6.1 Best subset selection (알고리즘 6.1 최적 부분집합 선택)

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모든 K크기의 변수 조합에서 가장 좋은 모델을 기록하고, 최종적으로 Cross-Validation이나 AIC/BIC 평가지표로 모델 1개를 선정하는 절차입니다.