rfcv2: Customized function for training random forest

In jitonglou/MultiMlearn: Statistical Learning Methods for Optimizing Individualized Treatment Rules

Description

rfcv2 creates a random forest model which has both mtry and ntree as tuning parameters for cross-validations. This function is an extension to random forest models that are currently supported by the train function of the caret package as all of those models just use mtry.

Usage

rfcv2(type)

Arguments

type	the type of the prediction problem. One of Regression and Classification.

Value

A function to be used in the train function of the caret package.

Examples

library(caret)
library(randomForest)
library(mlbench)

#######################################
## Classification Example
data(iris)

set.seed(0)
rf_class_fit = train(Species ~ .,
                     data=iris,
                     method=rfcv2("Classification"),
                     tuneGrid=expand.grid(
                       .mtry=seq(1,ncol(iris)-1, 1),
                       .ntree=seq(100,500,100)),
                     trControl=trainControl(method="cv"))
print(rf_class_fit)

#######################################
## Regression Example
data(BostonHousing)

set.seed(0)
rf_reg_fit = train(medv ~ .,
                   data = BostonHousing,
                   method=rfcv2("Regression"),
                   tuneGrid=expand.grid(
                     .mtry=seq(1,sqrt(ncol(BostonHousing)-1), 1),
                     .ntree=seq(100,500,100)),
                   trControl=trainControl(method="cv"))
print(rf_reg_fit)

jitonglou/MultiMlearn documentation built on Dec. 21, 2021, 12:08 a.m.