Machine learning

Creating a Random Forest model

One example of machine learning algorithms is the Random Forest alogrithm (Breiman, L. (2001). Random Forests. Machine Learning 45(5), p. 5-32). This algorithm is implemented in R according to Breiman’s original Fortran implementation in the randomForest package.

Random Forest classifier objects can be created in R by preparing the class variable as factor, which is already apparent in the iris data set. Therefore we can easily create a Random Forest by:

library(randomForest)

rf <- randomForest(x = iris[, 1:4], 
                   y = iris$Species, 
                   ntree = 500, 
                   do.trace = 100)

rf

# Call:
#   randomForest(x = iris[, 1:4], y = iris$Species, ntree = 500,      do.trace = 100) 
# Type of random forest: classification
# Number of trees: 500
# No. of variables tried at each split: 2
# 
# OOB estimate of  error rate: 4%
# Confusion matrix:
#   setosa versicolor virginica class.error
# setosa         50          0         0        0.00
# versicolor      0         47         3        0.06
# virginica       0          3        47        0.06

parameters	Description
x	a data frame holding the describing variables of the classes
y	the classes of the individual obserbations. If this vector is `factor`, a classification model is created, if not a regression model is created.
ntree	The number of individual CART trees built
do.trace	every ith step, the out-of-the-box errors overall and for each class are returned