recitation 4 m ay 23
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
RECITATION 4MAY 23
DPMM
Splines with multiple predictors
Classification and regression trees
Dirichlet Process Mixture Model
• Library “DPpackage”
• R Demo 1
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:
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
Classification and regression trees
• Classification tree• The response is binary or categorical outcome.
• 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
Course Evaluation• Thanks!