MachineShop (version 2.6.0)

RandomForestModel: 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 = NULL
)

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.

Value

MLModel class object.

Details

Response Types:

factor, numeric

Automatic Tuning of Grid Parameters:

mtry, nodesize*

* included only in randomly sampled grid points

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

See Also

randomForest, fit, resample

Examples

# NOT RUN {
fit(sale_amount ~ ., data = ICHomes, model = RandomForestModel)

# }