RandomForestModel: Random Forest Model

In MachineShop: Machine Learning Models and Tools

Random Forest Model

Description

Implementation of Breiman's random forest algorithm (based on Breiman and Cutler's original Fortran code) for classification and regression.

Usage

RandomForestModel(
  ntree = 500,
  mtry = .(if (is.factor(y)) floor(sqrt(nvars)) else max(floor(nvars/3), 1)),
  replace = TRUE,
  nodesize = .(if (is.factor(y)) 1 else 5),
  maxnodes = integer()
)

Arguments

ntree	number of trees to grow.
mtry	number of variables randomly sampled as candidates at each split.
replace	should sampling of cases be done with or without replacement?
nodesize	minimum size of terminal nodes.
maxnodes	maximum number of terminal nodes trees in the forest can have.

Details

Response types:

factor, numeric

Automatic tuning of grid parameters:

mtry, nodesize*

* 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

randomForest, fit, resample

Examples

## Requires prior installation of suggested package randomForest to run

fit(sale_amount ~ ., data = ICHomes, model = RandomForestModel)

MachineShop documentation built on Sept. 11, 2024, 6:28 p.m.