Machine Learning

K-Means Clustering/Hierarchical Clustering Algorithm ...

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, $$ \begin{align} Z_m=\sum_{j=1}^p\phi_{jm}X_j \end{align} $$ ...

Maximal Margin Classifier What Is a Hyperplane? Hyperplane: In a p-dimensional space, a hyperplane is a flat affine subspace of dimension $p − 1$. e.g. in two dimensions, a hyperplane is a flat one-dimensional subspace—in other words, a line. Mathematical definition of a hyperplane: $$ \beta_0+\beta_1X_1+\beta_2X_2,…+\beta_pX_p=0, \quad (9.1) $$ Any $X = (X_1,X_2,…X_p)^T$ for which (9.1) holds is a point on the hyperplane. ...

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 ...