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203.3.7 Building a Decision Tree in R

Machine learning with R

LAB: Decision Tree Building

In previous section, we studied about Information Gain in Decision Tree Split

  • Import Data:Ecom_Cust_Relationship_Management/Ecom_Cust_Survey.csv
  • How many customers have participated in the survey?
  • Overall most of the customers are satisfied or dis-satisfied?
  • Can you segment the data and find the concentrated satisfied and dis-satisfied customer segments ?
  • What are the major characteristics of satisfied customers?
  • What are the major characteristics of dis-satisfied customers?

Solution

Ecom_Cust_Survey <- read.csv("C:\\Amrita\\Datavedi\\Ecom_Cust_Relationship_Management\\Ecom_Cust_Survey.csv")
  • How many customers have participated in the survey?
nrow(Ecom_Cust_Survey)
## [1] 11812
  • Overall most of the customers are satisfied or dis-satisfied?
table(Ecom_Cust_Survey$Overall_Satisfaction)
## 
## Dis Satisfied     Satisfied 
##          6411          5401

Code-Decision Tree Building

rpart(formula, method, data, control)

  • Formula : y~x1+x2+x3
  • method: “Class” for classification trees , “anova” for regression trees with continuous output
  • For controlling tree growth. For example, control=rpart.control(minsplit=30, cp=0.001)
  • Minsplit : Minimum number of observations in a node be 30 before attempting a split
  • A split must decrease the overall lack of fit by a factor of 0.001 (cost complexity factor) before being attempted.(details later)
  • Need the library rpart
library(rpart)
  • Building Tree Model
Ecom_Tree<-rpart(Overall_Satisfaction~Region+ Age+ Order.Quantity+Customer_Type+Improvement.Area, method="class", data=Ecom_Cust_Survey)
Ecom_Tree
## n= 11812 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 11812 5401 Dis Satisfied (0.542753132 0.457246868)  
##   2) Order.Quantity< 40.5 7404 1027 Dis Satisfied (0.861291194 0.138708806)  
##     4) Age>=29.5 7025  652 Dis Satisfied (0.907188612 0.092811388) *
##     5) Age< 29.5 379    4 Satisfied (0.010554090 0.989445910) *
##   3) Order.Quantity>=40.5 4408   34 Satisfied (0.007713249 0.992286751) *

Plotting the Trees

plot(Ecom_Tree, uniform=TRUE)
text(Ecom_Tree, use.n=TRUE, all=TRUE)

A better looking tree

library(rpart.plot)
## Warning: package 'rpart.plot' was built under R version 3.1.3
prp(Ecom_Tree,box.col=c("Grey", "Orange")[Ecom_Tree$frame$yval],varlen=0, type=1,extra=4,under=TRUE)

Tree Validation

  • Accuracy=(TP+TN)/(TP+FP+FN+TN)
  • Misclassification Rate=(FP+FN)/(TP+FP+FN+TN)

 

The next post is about Validating a Tree.

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