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-any)DidacticBoost_0.1.1.zip(r-4.6-any)DidacticBoost_0.1.1.zip(r-4.5-any)
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✨
DESCRIPTION
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 282 downloads 3 exports 1 dependencies

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

TargetResultTimeFilesSyslog
linux-develWARNING95
source / vignettesOK173
linux-releaseWARNING87
macos-releaseWARNING186
macos-oldrelWARNING203
windows-develWARNING61
windows-releaseWARNING52
windows-oldrelWARNING70
wasm-releaseOK95

Exports:fitBoostedis.boostedpredict.boosted

Dependencies:rpart