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DidacticBoost (version 0.1.1)

A Simple Implementation and Demonstration of Gradient Boosting

Description

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.

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Install

install.packages('DidacticBoost')

Monthly Downloads

130

Version

0.1.1

License

GPL-3

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Last Published

April 19th, 2016

Functions in DidacticBoost (0.1.1)

predict.boosted

Model Predictions
is.boosted

Is the Object a Boosted Model
fitBoosted

Simple Gradient Boosting