GBMModel: Generalized Boosted Regression Model

In brian-j-smith/MachineShop: Machine Learning Models and Tools

Generalized Boosted Regression Model

Description

Fits generalized boosted regression models.

Usage

GBMModel(
  distribution = character(),
  n.trees = 100,
  interaction.depth = 1,
  n.minobsinnode = 10,
  shrinkage = 0.1,
  bag.fraction = 0.5
)

Arguments

distribution	optional character string specifying the name of the distribution to use or list with a component name specifying the distribution and any additional parameters needed. Set automatically according to the class type of the response variable.
n.trees	total number of trees to fit.
interaction.depth	maximum depth of variable interactions.
n.minobsinnode	minimum number of observations in the trees terminal nodes.
shrinkage	shrinkage parameter applied to each tree in the expansion.
bag.fraction	fraction of the training set observations randomly selected to propose the next tree in the expansion.

Details

Response types:

factor, numeric, PoissonVariate, Surv

Automatic tuning of grid parameters:

n.trees, interaction.depth, shrinkage*, n.minobsinnode*

* excluded from grids by default

Default argument values and further model details can be found in the source See Also link below.

Value

MLModel class object.

See Also

gbm, fit, resample

Examples

## Requires prior installation of suggested package gbm to run

fit(Species ~ ., data = iris, model = GBMModel)

brian-j-smith/MachineShop documentation built on Sept. 22, 2023, 10:01 p.m.