Contrast Trees and Boosting
Build contrast tree
Contrast and Boosted Trees
Return the one sample parameters used in fortran discrepancy functions
Get terminal node observation assignments
Produce lack-of-fit curve for a contrast tree
Summarize contrast tree
Predict y-values from boosted contrast model
Prune a contrast tree
Show all possible pruned subtrees
Save the function f for calling from fortran
Print terminal node x-region boundaries
Cross-validate boosted contrast tree boosted with (new) data
Transform z-values t(z) such that the distribution of ap...
Contrast trees represent a new approach for assessing the accuracy of many types of machine learning estimates that are not amenable to standard (cross) validation methods; see "Contrast trees and distribution boosting", Jerome H. Friedman (2020) <doi:10.1073/pnas.1921562117>. In situations where inaccuracies are detected, boosted contrast trees can often improve performance. Functions are provided to to build such trees in addition to a special case, distribution boosting, an assumption free method for estimating the full probability distribution of an outcome variable given any set of joint input predictor variable values.