DidacticBoost: A Simple Implementation and Demonstration of Gradient Boosting

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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.

Author
David Shaub [aut, cre]
Date of publication
2016-04-19 08:11:59
Maintainer
David Shaub <davidshaub@gmx.com>
License
GPL-3
Version
0.1.1
URLs

View on CRAN

Man pages

fitBoosted
Simple Gradient Boosting
is.boosted
Is the Object a Boosted Model
predict.boosted
Model Predictions

Files in this package

DidacticBoost
DidacticBoost/tests
DidacticBoost/tests/testthat.R
DidacticBoost/tests/testthat
DidacticBoost/tests/testthat/test-Main.R
DidacticBoost/NAMESPACE
DidacticBoost/R
DidacticBoost/R/Main.R
DidacticBoost/MD5
DidacticBoost/DESCRIPTION
DidacticBoost/man
DidacticBoost/man/fitBoosted.Rd
DidacticBoost/man/predict.boosted.Rd
DidacticBoost/man/is.boosted.Rd