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Recitation 4 M ay 23

Recitation 4 M ay 23. DPMM S plines with multiple predictors C lassification and regression trees. D irichlet Process Mixture Model. Library “ DPpackage ” R D emo 1. S pline method with multiple predictors. G eneralized Additive Model N atural Thin Plate Splines

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Recitation 4 M ay 23

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  1. Recitation 4May 23 DPMM Splines with multiple predictors Classification and regression trees

  2. Dirichlet Process Mixture Model • Library “DPpackage” • R Demo 1

  3. Spline method with multiple predictors • Generalized Additive Model • Natural Thin Plate Splines • The minimizer of (RSS+“bending energy”) among all interpolators with knots at the observations. • Form:

  4. Spline method with multiple predictors • Thin Plate Regression Splines • Optimal approximation of thin plate splines using low rank basis • No need to choose knots • Tensor Product Splines • Basis: product of basis (truncated spline) of each dimension • R Demo 2

  5. Classification and regression trees • Classification tree • The response is binary or categoricaloutcome. • Regression tree • The response is a continuous variable. The predicted value will be the same for all data points in a leaf node. • “Grow” the tree and then “prune” it by minimizing cross validation error • R Demo 3

  6. Course Evaluation • Thanks!

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