MachineShop: GLMBoostModel – R documentation

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GLMBoostModel

Gradient Boosting with Linear Models

Description

Gradient boosting for optimizing arbitrary loss functions where component-wise linear models are utilized as base-learners.

Usage

GLMBoostModel(
  family = NULL,
  mstop = 100,
  nu = 0.1,
  risk = c("inbag", "oobag", "none"),
  stopintern = FALSE,
  trace = FALSE
)

Arguments

`family`	optional `Family` object. Set automatically according to the class type of the response variable.
`mstop`	number of initial boosting iterations.
`nu`	step size or shrinkage parameter between 0 and 1.
`risk`	method to use in computing the empirical risk for each boosting iteration.
`stopintern`	logical inidicating whether the boosting algorithm stops internally when the out-of-bag risk increases at a subsequent iteration.
`trace`	logical indicating whether status information is printed during the fitting process.

Details

Response Types:: binary factor, BinomialVariate, NegBinomialVariate, numeric, PoissonVariate, Surv
Automatic Tuning of Grid Parameters:: mstop

Default values for the NULL arguments and further model details can be found in the source links below.

Value

MLModel class object.

Examples

## Requires prior installation of suggested package mboost to run

data(Pima.tr, package = "MASS")

fit(type ~ ., data = Pima.tr, model = GLMBoostModel)

MachineShop

Machine Learning Models and Tools

v2.8.0

GPL-3

Authors

Brian J Smith [aut, cre]

Initial release

2021-04-16