Dimension Reduction Methods Subset selection and shrinkage methods all use the original predictors, X1,X2, . . . , Xp. Dimension Reduction Methods transform the predictors and then fit a least squares model using the transformed variables. Approach Let $Z_1,Z_2, . . . ,Z_M$ represent $M < p$ linear combinations of our original $p$ predictors. That is, $$ \begin{align} Z_m=\sum_{j=1}^p\phi_{jm}X_j \end{align} $$ ...
Resampling methods:involve repeatedly drawing samples from a training set and refitting a mode of interest on each sample in order to obtain additional information about the fitted model. model assessment：the process of evaluating a model’s performance model selection：the process of selecting the proper level of flexibility for a model cross-validation: can be used to estimate the test error associated with a given statistical learning method in order to evaluate its performance, or to select the appropriate level of flexibility. bootstrap:provide a measure of accuracy of a parameter estimate or of a given selection statistical learning method. ...
Subset Selection/Adjusted $R^2$/Ridge/Lasso/SVD ...
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