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)

Peer review:

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

On CRAN:

gradient-boosting

3 exports 0.62 score 1 dependencies 1 scripts 144 downloads

Last updated 8 years agofrom:b24338489a. Checks:OK: 1 WARNING: 6. Indexed: yes.

Exports:fitBoostedis.boostedpredict.boosted

Dependencies:rpart