Decision tree algorithm in rstudio

School of Computer & Information Sciences

ITS 836 Data Science and Big Data Analytics

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HW07 Lecture 07 Classification

Questions

Perform the ID3 Algorithm

R exercise for Decision Tree section 7_1

Explain how Random Forest Algorithm works

Iris Dataset with Decision Tree vs Random Forest

R exercise for Naïve Bayes section 7_2

Analyze Classifier Performance section 7_3

Redo calculations for ID3 and Naïve Bayes for the Golf

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HW07-1 Apply ID3 Algorithm to demonstrate the Decision Tree for the data set

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http://www.cse.unsw.edu.au/~billw/cs9414/notes/ml/06prop/id3/id3.html

Select Size Color Shape
yes medium blue brick
yes small red sphere
yes large green pillar
yes large green sphere
no small red wedge
no large red wedge
no large red pillar
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HW07 Q 2

Analyze R code in section 7_1 to create the decision tree classifier for the dataset: bank_sample.csv

Create and Explain all plots an d results

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# install packages rpart,rpart.plot

# put this code into Rstudio source and execute lines via Ctrl/Enter

library("rpart")

library("rpart.plot")

setwd("c:/data/rstudiofiles/")

banktrain <- read.table("bank-sample.csv",header=TRUE,sep=",")

## drop a few columns to simplify the tree

drops<-c("age", "balance", "day", "campaign", "pdays", "previous", "month")

banktrain <- banktrain [,!(names(banktrain) %in% drops)]

summary(banktrain)

# Make a simple decision tree by only keeping the categorical variables

fit <- rpart(subscribed ~ job + marital + education + default + housing + loan + contact + poutcome,method="class",data=banktrain,control=rpart.control(minsplit=1),

parms=list(split='information'))

summary(fit)

# Plot the tree

rpart.plot(fit, type=4, extra=2, clip.right.labs=FALSE, varlen=0, faclen=3)

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HW07 Q 2

Analyze R code in section 7_1 to create the decision tree classifier for the dataset: bank_sample.csv

Create and Explain all plots an d results

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HW07 Q 2

Analyze R code in section 7_1 to create the decision tree classifier for the dataset: bank_sample.csv

Create and Explain all plots and results

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6

HW 7 Q3

Explain how a Random Forest Algorithm Works

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http://blog.citizennet.com/blog/2012/11/10/random-forests-ensembles-and-performance-metrics

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Use Decision Tree Classifier and Random Forest

Attributes: sepal length, sepal width, petal length, petal width

All flowers contain a sepal and a petal

For the iris flowers three categories (Versicolor, Setosa, Virginica) different measurements

R.A. Fisher, 1936

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HW07 Q4 Using Iris Dataset

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HW07 Q4 Using Iris Dataset

Decision Tree applied to Iris Dataset

https://rpubs.com/abhaypadda/k-nn-decision-tree-on-IRIS-dataset or

https://davetang.org/muse/2013/03/12/building-a-classification-tree-in-r/

What are the disadvantages of Decision Trees?

https://www.quora.com/What-are-the-disadvantages-of-using-a-decision-tree-for-classification

Random Forest applied to Iris Dataset and compare to

https://rpubs.com/rpadebet/269829

http://rischanlab.github.io/RandomForest.html

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Get data and e1071 package

sample<-read.table("sample1.csv",header=TRUE,sep=",")

traindata<-as.data.frame(sample[1:14,])

testdata<-as.data.frame(sample[15,])

traindata #lists train data

testdata #lists test data, no Enrolls variable

install.packages("e1071", dep = TRUE)

library(e1071) #contains naïve Bayes function

model<-naiveBayes(Enrolls~Age+Income+JobSatisfaction+Desire,traindata)

model # generates model output

results<-predict(model,testdata)

Results # provides test prediction

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Q5 HW07 Section 7.2 Naïve Bayes in R

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7.3 classifier performance

# install some packages

install.packages("ROCR")

library(ROCR)

# training set

banktrain <- read.table("bank-sample.csv",header=TRUE,sep=",")

# drop a few columns

drops <- c("balance", "day", "campaign", "pdays", "previous", "month")

banktrain <- banktrain [,!(names(banktrain) %in% drops)]

# testing set

banktest <- read.table("bank-sample-test.csv",header=TRUE,sep=",")

banktest <- banktest [,!(names(banktest) %in% drops)]

# build the na?ve Bayes classifier

nb_model <- naiveBayes(subscribed~.,

data=banktrain)

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# perform on the testing set

nb_prediction <- predict(nb_model,

# remove column "subscribed"

banktest[,-ncol(banktest)],

type='raw')

score <- nb_prediction[, c("yes")]

actual_class <- banktest$subscribed == 'yes'

pred <- prediction(score, actual_class)

perf <- performance(pred, "tpr", "fpr")

plot(perf, lwd=2, xlab="False Positive Rate (FPR)",

ylab="True Positive Rate (TPR)")

abline(a=0, b=1, col="gray50", lty=3)

## corresponding AUC score

auc <- performance(pred, "auc")

auc <- unlist(slot(auc, "y.values"))

auc

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7.3 Diagnostics of Classifiers

We cover three classifiers

Logistic regression, decision trees, naïve Bayes

Tools to evaluate classifier performance

Confusion matrix

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7.3 Diagnostics of Classifiers

Bank marketing example

Training set of 2000 records

Test set of 100 records, evaluated below

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HW07 Q07 Review calculations for the ID3 and Naïve Bayes Algorithm

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Record OUTLOOK TEMPERATURE HUMIDITY WINDY PLAY GOLF
X0 Rainy Hot High False No
X1 Rainy Hot High True No
X2 Overcast Hot High False Yes
X3 Sunny Mild High False Yes
4 Sunny Cool Normal False Yes
5 Sunny Cool Normal True No
6 Overcast Cool Normal True Yes
7 Rainy Mild High False No
8 Rainy Cool Normal False Yes
9 Sunny Mild Normal False Yes
10 Rainy Mild Normal True Yes
11 Overcast Mild High True Yes
12 Overcast Hot Normal False Yes
X13 Sunny Mild High True No
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Questions?