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-4.5)DidacticBoost_0.1.1.zip(r-4.4)DidacticBoost_0.1.1.zip(r-4.3)
DidacticBoost_0.1.1.tgz(r-4.4-any)DidacticBoost_0.1.1.tgz(r-4.3-any)
DidacticBoost_0.1.1.tar.gz(r-4.5-noble)DidacticBoost_0.1.1.tar.gz(r-4.4-noble)
DidacticBoost_0.1.1.tgz(r-4.4-emscripten)DidacticBoost_0.1.1.tgz(r-4.3-emscripten)
DidacticBoost.pdf |DidacticBoost.html
DidacticBoost/json (API)

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

Peer review:

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

On CRAN:

gradient-boosting

2.70 score 1 scripts 146 downloads 3 exports 1 dependencies

Last updated 9 years agofrom:b24338489a. Checks:OK: 1 WARNING: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 22 2024
R-4.5-winWARNINGNov 22 2024
R-4.5-linuxWARNINGNov 22 2024
R-4.4-winWARNINGNov 22 2024
R-4.4-macWARNINGNov 22 2024
R-4.3-winWARNINGNov 22 2024
R-4.3-macWARNINGNov 22 2024

Exports:fitBoostedis.boostedpredict.boosted

Dependencies:rpart