GAMBoostModel: Gradient Boosting with Additive Models

Description Usage Arguments Details Value See Also Examples

View source: R/ML_GAMBoostModel.R

Description

Gradient boosting for optimizing arbitrary loss functions, where component-wise arbitrary base-learners, e.g., smoothing procedures, are utilized as additive base-learners.

Usage

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GAMBoostModel(
  family = NULL,
  baselearner = c("bbs", "bols", "btree", "bss", "bns"),
  dfbase = 4,
  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.

baselearner

character specifying the component-wise base learner to be used.

dfbase

gobal degrees of freedom for P-spline base learners ("bbs").

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.

See Also

gamboost, Family, baselearners, fit, resample

Examples

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## Requires prior installation of suggested package mboost to run

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

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

MachineShop documentation built on June 18, 2021, 9:06 a.m.