R Language Machine learning Creating a Random Forest model
Example
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 |
