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-4.5-any)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'))

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

On CRAN:

Conda:

gradient-boosting

2.70 score 1 scripts 149 downloads 3 exports 1 dependencies

Last updated 9 years agofrom:b24338489a. Checks:1 OK, 8 WARNING. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 22 2025
R-4.5-winWARNINGMar 22 2025
R-4.5-macWARNINGMar 22 2025
R-4.5-linuxWARNINGMar 22 2025
R-4.4-winWARNINGMar 22 2025
R-4.4-macWARNINGMar 22 2025
R-4.4-linuxWARNINGMar 22 2025
R-4.3-winWARNINGMar 22 2025
R-4.3-macWARNINGMar 22 2025

Exports:fitBoostedis.boostedpredict.boosted

Dependencies:rpart