1 / 6

DPMM Splines for Classification and Regression Analysis in R

Explore the use of Dirichlet Process Mixture Model (DPMM) splines with multiple predictors in R for classification and regression tasks. Learn about the DPpackage library, Thin Plate Regression Splines, Classification and Regression Trees, and more. No need to manually select knots with this approach. Discover optimal approximation of thin plate splines and tensor product splines for efficient modeling. Use R Demo 3 for a course evaluation. Thanks for learning!

washi
Télécharger la présentation

DPMM Splines for Classification and Regression Analysis in R

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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!

More Related