#The following algorithm obtains two classification trees for the Iris dataset.
#Functions rpart() and rpartXse() use the standard formula interface that most modeling functions
#in R use. This means specifying the abstract functional form of the model we are trying
#to obtain in the first argument and the available training data in the second.
#install.packages("devtools")
#library(devtools)
#install_github("ltorgo/DMwR2", ref="master")
library(DMwR2)
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
set.seed(1234)
data(iris)
ct1 <- rpartXse(Species ~ ., iris)
ct2 <- rpartXse(Species ~ ., iris, se=0)
#The first tree is obtained with the default parameters of rpartXse(), which means using
#1-SE post pruning.
#The second tree is obtained with a less “aggressive” pruning by specifying 0-SE pruning.
#This corresponds to selecting the lowest estimated error subtree of the originally overly large tree.
#Note the use of the function set.seed() as a way of ensuring you get the same trees.
#We will get a graphical representation of the two objects(ct1 and ct2), which will give us a
#textual representation of the trees.
#Package rpart.plot - provides powerful graphical visualizations for rpart trees.
#Namely, function prp() can be used to plot the trees with many graphical variants,
#accessible through the large amount of parameters of this function.
#The tree diagram shows the two trees of the above example.
library(rpart.plot)
## Warning: package 'rpart.plot' was built under R version 4.5.1
## Loading required package: rpart
## Warning: package 'rpart' was built under R version 4.5.1
prp(ct1, type=0, extra=101) # left tree
## Warning: Cannot retrieve the data used to build the model (so cannot determine roundint and is.binary for the variables).
## To silence this warning:
## Call prp with roundint=FALSE,
## or rebuild the rpart model with model=TRUE.
prp(ct2, type=0, extra=101) # right tree
## Warning: Cannot retrieve the data used to build the model (so cannot determine roundint and is.binary for the variables).
## To silence this warning:
## Call prp with roundint=FALSE,
## or rebuild the rpart model with model=TRUE.
#Regression trees are obtained using the same procedure.
#The function, rpart() decides to obtain either a classification or a regression tree,
#depending on the type of the target variable you indicate in the formula.
#Trees obtained with this package can also be used to obtain predictions for a set of test cases.
#Package rpart uses the standard procedure for providing a predict() methodfor the objects produced by
#the rpart() function.
#The usage of this predict() function requires you to specify the model in the first argument and the
#test cases (a data frame) in the second. Notice the example coding below:
set.seed(1234)
rndSample <- sample(1:nrow(iris), 100)
tr <- iris[rndSample, ]
ts <- iris[- rndSample, ]
ct <- rpartXse(Species ~ ., tr, se=0.5)
psi <- predict(ct, ts)
head(psi)
## setosa versicolor virginica
## 1 1 0 0
## 7 1 0 0
## 11 1 0 0
## 12 1 0 0
## 15 1 0 0
## 16 1 0 0
ps2 <- predict(ct, ts, type="class")
head(ps2)
## 1 7 11 12 15 16
## setosa setosa setosa setosa setosa setosa
## Levels: setosa versicolor virginica
(cm <- table(ps2, ts$Species))
##
## ps2 setosa versicolor virginica
## setosa 18 0 0
## versicolor 0 15 1
## virginica 0 3 13
100*(1-sum(diag(cm))/sum(cm)) # the error rate
## [1] 8
#If called without any further argument with a classification tree in the first argument,
#the predict() method of these rpart objects will output a matrix with the estimated probabilities
#of each class for each test case.
#With the extra argument type=“class” we get a vector with the predicted classes for all test cases
#(which are the classes with highest probability).
#To obtain predictions with regression trees, we use the same predict() function. However, in this
#case, the argument type=“class” does not make sense as the predictions are numbers and there are
#no class probabilities.