We will use R.A. Fisher’s classic iris data set to generate a classification tree. This data set is freely available from the UCI Machine Learing Repository, https://archive.ics.uci.edu/ml/datasets/Iris.

The data set includes:

  1. Sepal length in cm
  2. Sepal width in cm
  3. Petal length in cm
  4. Petal width in cm

  5. class:

    -Iris setosa

    -Iris versicolor

    -Iris virginica

Data Exploration

#Load data

data(iris)
nrow <- nrow(iris); ncol <- ncol(iris)  #inline code
iris[1:4,]  # View first four rowss of data set
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa

Analysis

Analysis consisted of fitting a predictive model of iris species based on petal length/width and sepal length/width using the caret package.

#Load required package:
library(caret)
modFit <- train(Species ~., method = "rpart", data=iris) #Fit model
print(modFit$finalModel)   #Summarize model
## n= 150 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 150 100 setosa (0.33333 0.33333 0.33333)  
##   2) Petal.Length< 2.45 50   0 setosa (1.00000 0.00000 0.00000) *
##   3) Petal.Length>=2.45 100  50 versicolor (0.00000 0.50000 0.50000)  
##     6) Petal.Width< 1.75 54   5 versicolor (0.00000 0.90741 0.09259) *
##     7) Petal.Width>=1.75 46   1 virginica (0.00000 0.02174 0.97826) *

The final analysis is presented as a decison tree, using the rattle package.

library(rattle)
fancyRpartPlot(modFit$finalModel) # Plot decision tree

plot of chunk plotTree

Discussion

I. setosa can be successfully isolated from other species by petal length. I. versicolor and I. virgicina are somewhat more difficult to separate based on petal width.

This document is hosted freely on RPubs: http://rpubs.com/BlackWidoww/58757

.Rmd source code can be found here: https://github.com/BlackWidoww/ABG250