A Tutorial
Package: conTree 0.3-1
conTree: Contrast Trees and Boosting
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.
Authors:
conTree_0.3-1.tar.gz
conTree_0.3-1.zip(r-4.7-x86_64)conTree_0.3-1.zip(r-4.7-arm64)conTree_0.3-1.zip(r-4.6-x86_64)conTree_0.3-1.zip(r-4.6-arm64)conTree_0.3-1.zip(r-4.5-x86_64)
conTree_0.3-1.tgz(r-4.6-x86_64)conTree_0.3-1.tgz(r-4.6-arm64)conTree_0.3-1.tgz(r-4.5-x86_64)conTree_0.3-1.tgz(r-4.5-arm64)
conTree_0.3-1.tar.gz(r-4.7-arm64)conTree_0.3-1.tar.gz(r-4.7-x86_64)conTree_0.3-1.tar.gz(r-4.6-arm64)conTree_0.3-1.tar.gz(r-4.6-x86_64)
conTree_0.3-1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
conTree/json (API)
| # Install 'conTree' in R: |
| install.packages('conTree', repos = c('https://bnaras.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/bnaras/contree/issues
Datasets:
- age_data - Age and Demographics data
- air_quality - Air Quality Data from UC Irvine Machine Learning Repository
- census - Census Data Example from UC Irvine Machine Learning Repository
Last updated from:0b9340f229. Checks:14 OK, 1 WARNING. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 86 | ||
| linux-devel-x86_64 | OK | 107 | ||
| source / vignettes | OK | 170 | ||
| linux-release-arm64 | OK | 89 | ||
| linux-release-x86_64 | OK | 96 | ||
| macos-release-arm64 | OK | 104 | ||
| macos-release-x86_64 | OK | 186 | ||
| macos-oldrel-arm64 | OK | 112 | ||
| macos-oldrel-x86_64 | OK | 264 | ||
| windows-devel-arm64 | OK | 195 | ||
| windows-devel-x86_64 | OK | 118 | ||
| windows-release-arm64 | WARNING | 171 | ||
| windows-release-x86_64 | OK | 83 | ||
| windows-oldrel-x86_64 | OK | 105 | ||
| wasm-release | OK | 76 |
Exports:bootcricontrastgetnodeslofcurvemodtrastnodeplotsnodesumonesample_parameterspredtrastpruneprune.seqsave_rfuntreesumtwosample_parametersxvalydist
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Contrast and Boosted Trees | conTree-package |
| Age and Demographics data | age_data |
| Air Quality Data from UC Irvine Machine Learning Repository | air_quality |
| Census Data Example from UC Irvine Machine Learning Repository | census |
| Build contrast tree | bootcri contrast modtrast |
| Get terminal node observation assignments | getnodes |
| Produce lack-of-fit curve for a contrast tree | lofcurve |
| Summarize contrast tree | nodeplots nodesum |
| Return the one sample parameters used in fortran discrepancy functions | onesample_parameters twosample_parameters |
| Predict y-values from boosted contrast model | predtrast |
| Prune a contrast tree | prune |
| Show all possible pruned subtrees | prune.seq |
| Save the function f for calling from fortran | save_rfun |
| Print terminal node x-region boundaries | treesum |
| Cross-validate boosted contrast tree boosted with (new) data | xval |
| Transform z-values t(z) such that the distribution of p(t(z) | x) approximates p(t(y | x) for type = 'dist' only | ydist |