decisionTrees.md

In MickyDowns/mine_algorithms: What the package does (short line)

Decision Tree code/examples

This is an R Markdown document tracks R examples, exercises used in Stats202 lectures.

Decision trees

Function "rpart" generated decision trees in R. Does classification and regression. For classification, ensure rpart() knows you're predicting (y) a factor, not numeric, variable.

library(rpart)

Read sonar datasets of 60 attr and 100 obs.

setwd("./data")
##download.file("nice redirects google","sonar_train.csv", method="curl")
##download.file("https://sites.google.com/site/stats202/data/sonar_train.csv",
##              "sonar_test.csv",method="curl")

train<-read.csv("sonar_train.csv",header=F)
test<-read.csv("sonar_test.csv",header=F)

setwd("../")

Fit a decision tree to predict where obj is metal or a rock.

y<-as.factor(train[,61])
x<-train[,1:60]
fit<-rpart(y~.,x)

## or fit1<-rpart(as.factor(train$V61)~.,data=train)

How accurate did predict y

sum(y!=predict(fit,x,type="class"))/length(y)

## [1] 0.1154

## predict(fit,x,type="class") yields what you were trying to predict e.g., column 61. 
## So, take sum of instances where prediction != col61 and divide by total obs.
## Yields training set error rate. 

MickyDowns/mine_algorithms documentation built on May 8, 2019, 10:49 a.m.