README.md

In mananshah99/rotationforest: An implementation of the "Rotation Forest" algorithm from Rodriguez et al. 2006 in R

rotationforest

You can use the package rotationForest in R to accomplish this task.

1. Install

install.packages('devtools') # Only if needed
require('devtools')
devtools::install_github('mananshah99/rotationforest')
require('rotationForest')

2. Usage

Sample usage is demonstrated below:

fpath <- system.file("extdata", "balance-scale.data", package="rotationForest")
data <- read.table(fpath, sep = ",", header = FALSE)
data.dependent <- data[,-1]
data.response <- data[,1]
data.response <- as.factor(data.response)
total <- data.frame(data.response, data.dependent)
groups <- sample(rep(1:10, times = ceiling(nrow(total) / 19)), size = nrow(total), replace = TRUE)
data.train <- total[!groups %in% 1,]
data.test <- total[groups %in% 1,]
fit <- rotationForest(data.train[,-1], data.train[,1], 2, 10)
predict <- predict(fit, data.dependent, prob = FALSE)

The documentation describes in closer detail the arguments and functionality of rotationForest and the overloaded predict.

mananshah99/rotationforest documentation built on May 21, 2019, 11:23 a.m.