MachineShop (version 1.1.0)

GAMBoostModel: Gradient Boosting with Additive Models

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

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

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.

Value

MLModel class object.

Details

Response Types:

binary, numeric, Surv

Automatic Tuning Grid Parameters:

mstop

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

See Also

gamboost, Family, baselearners, fit, resample, tune

Examples

# NOT RUN {
library(MASS)

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

# }