Package: DidacticBoost 0.1.1

DidacticBoost: A Simple Implementation and Demonstration of Gradient Boosting

A basic, clear implementation of tree-based gradient boosting designed to illustrate the core operation of boosting models. Tuning parameters (such as stochastic subsampling, modified learning rate, or regularization) are not implemented. The only adjustable parameter is the number of training rounds. If you are looking for a high performance boosting implementation with tuning parameters, consider the 'xgboost' package.

Authors:David Shaub [aut, cre]

DidacticBoost_0.1.1.tar.gz
DidacticBoost_0.1.1.zip(r-4.7)DidacticBoost_0.1.1.zip(r-4.6)DidacticBoost_0.1.1.zip(r-4.5)
DidacticBoost_0.1.1.tgz(r-4.6-any)DidacticBoost_0.1.1.tgz(r-4.5-any)
DidacticBoost_0.1.1.tar.gz(r-4.7-any)DidacticBoost_0.1.1.tar.gz(r-4.6-any)
DidacticBoost_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
DidacticBoost/json (API)

# Install 'DidacticBoost' in R:
install.packages('DidacticBoost', repos = c('https://dashaub.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/dashaub/didacticboost/issues

On CRAN:

Conda:

gradient-boosting

2.70 score 1 scripts 206 downloads 3 exports 1 dependencies

Last updated from:b24338489a. Checks:7 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64WARNING145
source / vignettesOK147
linux-release-x86_64WARNING107
macos-release-arm64WARNING145
macos-oldrel-arm64WARNING188
windows-develWARNING70
windows-releaseWARNING61
windows-oldrelWARNING65
wasm-releaseOK108

Exports:fitBoostedis.boostedpredict.boosted

Dependencies:rpart