Random forests

The random forests algorithm attempts a number of random decision trees and provides the tree that works best within the parameters used to drive the model.

Random forests in R

With R we include the packages we are going to use:

install.packages("randomForest", repos="http://cran.r-project.org") 
library(randomForest) 

Load the data:

filename = "http://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data" 
housing <- read.table(filename) 
colnames(housing) <- c("CRIM", "ZN", "INDUS", "CHAS", "NOX",  
                       "RM", "AGE", "DIS", "RAD", "TAX", "PRATIO", 
                       "B", "LSTAT", "MDEV") 

Split it up:

housing <- housing[order(housing$MDEV),] 
#install.packages("caret") 
library(caret) 
set.seed(5557) 
indices <- createDataPartition(housing$MDEV, p=0.75, list=FALSE) 
training <- housing[indices,] 
testing <- housing[-indices,] 
nrow(training) 
nrow(testing) 

Calculate our model:

forestFit <- randomForest(MDEV ~ CRIM ...