setwd("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TF/10-30")#install.packages("naivebayes")
library(naivebayes)## Warning: package 'naivebayes' was built under R version 3.4.3library(dplyr)## Warning: Installed Rcpp (0.12.16) different from Rcpp used to build dplyr (0.12.11).
## Please reinstall dplyr to avoid random crashes or undefined behavior.##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(psych)
library(ggplot2)##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alphalibrary(e1071)
library(readxl)#Import Labels
Labels <- read_excel("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/1.Labels/Source Data.xlsx")
Label <- Labels$Score#Import Features
Features <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TF/10-30/Feature Set 1: 10 to 30th Percentile.csv")
Features <- Features[-1]#Class 2
Label2 <- list()
for(i in 1:1000){
if(Label[i]==3| Label[i]==4){
Label2[i] <- 1
}else{
Label2[i] <- 0
}
}#As Factor
Label2 <- as.factor(unlist(Label2))#Class 3
Label3 <- list()
for(i in 1:1000){
if(Label[i]==5| Label[i]==6){
Label3[i] <- 1
}else{
Label3[i] <- 0
}
}#As Factor
Label3 <- as.factor(unlist(Label3))#Class 4
Label4 <- list()
for(i in 1:1000){
if(Label[i]==7| Label[i]==8){
Label4[i] <- 1
}else{
Label4[i] <- 0
}
}#As Factor
Label4 <- as.factor(unlist(Label4))#Class 5
Label5 <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
Label5[i] <- 1
}else{
Label5[i] <- 0
}
}#As Factor
Label5 <- as.factor(unlist(Label5))#All Labels
All <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
All[i] <- 5
}else if(Label[i]==7| Label[i]==8){
All[i] <- 4
}else if(Label[i]==5| Label[i]==6){
All[i] <- 3
}else{
All[i] <- 2
}
}#As Factor
All <- as.factor(unlist(All))#Control
Control.df <- data.frame(matrix(seq(1,1000),ncol=1,nrow=1000))
Control.df$Actual <- Label
Control.df$All <- All
Control.df$Label2 <- Label2
Control.df$Label3 <- Label3
Control.df$Label4 <- Label4
Control.df$Label5 <- Label5
Control.df[1:10,2:7]## Actual All Label2 Label3 Label4 Label5
## 1 3 2 1 0 0 0
## 2 8 4 0 0 1 0
## 3 7 4 0 0 1 0
## 4 4 2 1 0 0 0
## 5 7 4 0 0 1 0
## 6 7 4 0 0 1 0
## 7 5 3 0 1 0 0
## 8 10 5 0 0 0 1
## 9 7 4 0 0 1 0
## 10 8 4 0 0 1 0#Transform Integer to Factor
for(i in 1:1494){
Features[,i] <- as.factor(Features[,i])
}
str(Features)## 'data.frame': 1000 obs. of 1494 variables:
## $ abit : Factor w/ 3 levels "0","1","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ abl : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ absolut : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ accent : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ accept : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ access : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ accommod : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ ach : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ acknowledg : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ across : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ actual : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
## $ add : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ addit : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ adequ : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ adjac : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ adult : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ advanc : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ adverti : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
## $ advi : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ advic : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ affect : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ afford : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ afraid : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ...
## $ africa : Factor w/ 2 levels "0","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ after : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
## $ afternoon : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ ago : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ air : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ aircon : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ aircondit : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 2 ...
## $ airi : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ airport : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ alarm : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ albert : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ alcohol : Factor w/ 2 levels "0","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ all : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ alloc : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ allow : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ almost : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ along : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ alreadi : Factor w/ 3 levels "0","1","2": 1 1 1 2 1 1 1 1 1 1 ...
## $ also : Factor w/ 5 levels "0","1","2","3",..: 1 1 1 4 1 1 2 1 2 1 ...
## $ altern : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ although : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ alway : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ amaz : Factor w/ 3 levels "0","1","2": 1 1 1 1 2 1 1 1 1 1 ...
## $ ambienc : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ amen : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
## $ american : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ amount : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ amsterdam : Factor w/ 3 levels "0","1","2": 1 2 1 2 1 1 1 1 1 1 ...
## $ and : Factor w/ 3 levels "0","1","2": 2 1 1 1 1 1 1 1 1 1 ...
## $ angri : Factor w/ 2 levels "0","1": 2 1 1 2 1 1 1 1 1 1 ...
## $ anna : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ annoy : Factor w/ 3 levels "0","1","2": 2 1 1 1 1 1 1 1 1 1 ...
## $ anoth : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ answer : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ anymor : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ anyon : Factor w/ 3 levels "0","1","2": 1 2 1 1 1 1 1 1 1 1 ...
## $ anyth : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ anyway : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ anywh : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ apart : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 1 ...
## $ apolog : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ appal : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ appar : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ appeal : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ appear : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ appl : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ appoint : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ appreci : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ approach : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ apt : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ architectur : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ area : Factor w/ 3 levels "0","1","2": 1 1 1 2 1 1 1 1 1 1 ...
## $ aren : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ arena : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ around : Factor w/ 3 levels "0","1","2": 1 1 1 2 1 1 1 1 1 1 ...
## $ arrang : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ arriv : Factor w/ 4 levels "0","1","2","3": 2 1 1 1 2 1 1 1 1 1 ...
## $ art : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ asid : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
## $ ask : Factor w/ 3 levels "0","1","2": 2 1 2 1 1 1 1 1 1 1 ...
## $ asleep : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ aspect : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ assign : Factor w/ 2 levels "0","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ assist : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ assum : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ assur : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ ate : Factor w/ 2 levels "0","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ atm : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ atmosph : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ attend : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ attent : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ attic : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ attitud : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ attract : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ avail : Factor w/ 3 levels "0","1","2": 2 1 1 1 1 1 1 1 1 1 ...
## $ averag : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## [list output truncated]#Features
set.seed(1234)
ind <- sample(2,nrow(Features),replace = T, prob =c(0.8,0.2))
train <- Features[ind == 1,]
test <- Features[ind ==2,]train.labels.2 <- Label2[ind == 1]
test.labels.2 <- Label2[ind ==2]
train.labels.3 <- Label3[ind == 1]
test.labels.3 <- Label3[ind ==2]
train.labels.4 <- Label4[ind == 1]
test.labels.4 <- Label4[ind ==2]
train.labels.5 <- Label5[ind == 1]
test.labels.5 <- Label5[ind ==2]
train.labels <- All[ind == 1]
test.labels <- All[ind ==2]data.frame(train.labels,train.labels.2,train.labels.3,train.labels.4,train.labels.5)## train.labels train.labels.2 train.labels.3 train.labels.4
## 1 2 1 0 0
## 2 4 0 0 1
## 3 4 0 0 1
## 4 2 1 0 0
## 5 4 0 0 1
## 6 3 0 1 0
## 7 5 0 0 0
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## 792 1table(All)## All
## 2 3 4 5
## 34 112 309 545NB2 <- naiveBayes(x = train,y = train.labels.2)
NB3 <- naiveBayes(x = train,y = train.labels.3)
NB4 <- naiveBayes(x = train,y = train.labels.4)
NB5 <- naiveBayes(x = train,y = train.labels.5)NB.Pred2 <- predict(NB2, test,type ="raw")
NB.Pred3 <- predict(NB3, test,type ="raw")
NB.Pred4 <- predict(NB4, test,type ="raw")
NB.Pred5 <- predict(NB5, test,type ="raw")Voting.df <- data.frame(NB.Pred2, NB.Pred3,NB.Pred4,NB.Pred5)
colnames(Voting.df) <- c("Class 2: 0","Class2: 1","Class 3: 0","Class3: 1","Class 4: 0","Class4: 1","Class 5: 0","Class5: 1")
head(Voting.df)## Class 2: 0 Class2: 1 Class 3: 0 Class3: 1 Class 4: 0 Class4: 1
## 1 1.0000000 3.438826e-16 0.4613864 5.386136e-01 0.003138337 9.968617e-01
## 2 1.0000000 2.990306e-20 1.0000000 3.477072e-08 0.999990523 9.476521e-06
## 3 1.0000000 1.886816e-16 0.9998758 1.242268e-04 0.985512447 1.448755e-02
## 4 1.0000000 2.081719e-11 0.9999999 6.208422e-08 0.999918496 8.150432e-05
## 5 0.9999939 6.073596e-06 0.9598218 4.017823e-02 0.859589570 1.404104e-01
## 6 1.0000000 6.117880e-15 0.9591791 4.082089e-02 0.979909902 2.009010e-02
## Class 5: 0 Class5: 1
## 1 1.000000e+00 4.496208e-13
## 2 3.171695e-05 9.999683e-01
## 3 1.525431e-01 8.474569e-01
## 4 9.924418e-01 7.558163e-03
## 5 9.698277e-01 3.017233e-02
## 6 9.744822e-01 2.551777e-02Transformed.Voting.df <- Voting.df[seq(2,8,2)]
colnames(Transformed.Voting.df) <- c("2","3","4","5")
head(Transformed.Voting.df)## 2 3 4 5
## 1 3.438826e-16 5.386136e-01 9.968617e-01 4.496208e-13
## 2 2.990306e-20 3.477072e-08 9.476521e-06 9.999683e-01
## 3 1.886816e-16 1.242268e-04 1.448755e-02 8.474569e-01
## 4 2.081719e-11 6.208422e-08 8.150432e-05 7.558163e-03
## 5 6.073596e-06 4.017823e-02 1.404104e-01 3.017233e-02
## 6 6.117880e-15 4.082089e-02 2.009010e-02 2.551777e-02Evaluation <- Transformed.Voting.df
Index <- as.numeric(apply(Transformed.Voting.df,MARGIN = 1,which.max))
Index <- Index+1
Evaluation$Vote <- Index
Evaluation$Actual <- test.labelshead(Evaluation,100)## 2 3 4 5 Vote Actual
## 1 3.438826e-16 5.386136e-01 9.968617e-01 4.496208e-13 4 4
## 2 2.990306e-20 3.477072e-08 9.476521e-06 9.999683e-01 5 5
## 3 1.886816e-16 1.242268e-04 1.448755e-02 8.474569e-01 5 5
## 4 2.081719e-11 6.208422e-08 8.150432e-05 7.558163e-03 5 4
## 5 6.073596e-06 4.017823e-02 1.404104e-01 3.017233e-02 4 4
## 6 6.117880e-15 4.082089e-02 2.009010e-02 2.551777e-02 3 4
## 7 4.415321e-16 2.220561e-13 3.791909e-04 5.335329e-07 4 5
## 8 1.294350e-19 9.727644e-01 9.991492e-01 1.376992e-12 4 3
## 9 5.828943e-18 3.108151e-11 2.892882e-01 7.643042e-01 5 5
## 10 1.309899e-08 3.011940e-03 2.499868e-07 9.411633e-07 3 3
## 11 5.667822e-19 8.118526e-06 6.521061e-01 4.581823e-01 4 4
## 12 1.997478e-09 5.020479e-04 1.159748e-01 1.556989e-03 4 3
## 13 7.481226e-15 5.777920e-03 3.014822e-03 9.113464e-01 5 5
## 14 1.390860e-06 1.640896e-01 5.723371e-01 3.138224e-07 4 4
## 15 3.134540e-09 9.953848e-01 4.039486e-02 1.196266e-04 3 4
## 16 9.069421e-25 1.861475e-09 5.698366e-03 9.264731e-01 5 5
## 17 1.931691e-15 3.203977e-04 3.444752e-02 4.359744e-02 5 4
## 18 2.632066e-11 6.351855e-07 2.774968e-01 9.574705e-01 5 5
## 19 1.753629e-09 1.488664e-01 4.860885e-01 9.099060e-04 4 4
## 20 4.167222e-12 3.763179e-04 1.909394e-01 8.099695e-01 5 2
## 21 3.829176e-22 6.262764e-07 8.752340e-01 6.850146e-06 4 4
## 22 5.345881e-24 1.108034e-04 1.394871e-01 6.678916e-01 5 4
## 23 5.745854e-03 9.996790e-01 5.569008e-02 2.088253e-14 3 3
## 24 3.904629e-20 7.661734e-07 2.798614e-01 9.989329e-01 5 5
## 25 1.525079e-12 7.272426e-02 2.591973e-01 8.628604e-02 4 4
## 26 2.180082e-15 4.513008e-03 1.305986e-01 8.389885e-01 5 5
## 27 4.880568e-14 8.765901e-09 9.061044e-01 5.999800e-01 4 4
## 28 2.957185e-11 1.958394e-05 7.075859e-03 9.090646e-01 5 5
## 29 2.981750e-10 5.548549e-06 3.027098e-01 9.521318e-08 4 3
## 30 1.399654e-09 9.644282e-02 1.029453e-02 9.695980e-15 3 3
## 31 1.878414e-15 8.394779e-07 1.964245e-03 9.998253e-01 5 5
## 32 5.756952e-17 2.285659e-04 4.810187e-05 9.994335e-01 5 5
## 33 1.741036e-17 1.301160e-05 9.710890e-01 8.548886e-01 4 5
## 34 4.861800e-14 9.748044e-01 1.571259e-02 1.276190e-05 3 3
## 35 4.717549e-13 5.269934e-03 8.373793e-01 3.623790e-01 4 4
## 36 1.582690e-15 8.630958e-01 8.469807e-01 4.004209e-03 3 4
## 37 2.677338e-10 7.506911e-07 9.984960e-01 2.563193e-09 4 5
## 38 5.292306e-02 9.998664e-01 9.328485e-01 2.083549e-10 3 3
## 39 3.392938e-18 6.196608e-09 2.729805e-04 9.998583e-01 5 5
## 40 8.225312e-14 6.313385e-03 9.834256e-01 4.294418e-07 4 3
## 41 4.911999e-18 2.585669e-06 5.390409e-04 9.999697e-01 5 5
## 42 1.691079e-19 6.490421e-05 9.472429e-01 6.915164e-04 4 4
## 43 1.273987e-17 7.533363e-14 4.298562e-01 5.810057e-04 4 5
## 44 2.936045e-11 7.103548e-03 7.349908e-01 1.405711e-02 4 3
## 45 4.697088e-13 1.988116e-02 9.680444e-01 5.491885e-02 4 5
## 46 5.464961e-08 8.303622e-03 8.349188e-01 3.204377e-02 4 4
## 47 2.233089e-13 3.430205e-05 8.966067e-01 7.644584e-01 4 5
## 48 5.551745e-12 4.339753e-01 9.166518e-02 2.415049e-01 3 3
## 49 4.753627e-15 3.617091e-06 4.743048e-03 9.999746e-01 5 5
## 50 1.503650e-08 3.389719e-05 2.263311e-03 9.789930e-01 5 5
## 51 7.795847e-15 1.418735e-05 9.876589e-03 9.998978e-01 5 4
## 52 7.693985e-11 6.179108e-06 9.857240e-01 3.348726e-02 4 5
## 53 1.574940e-13 4.180098e-03 3.980479e-03 9.866040e-01 5 4
## 54 9.014502e-15 2.684521e-06 9.710412e-01 5.578270e-01 4 4
## 55 4.791961e-13 2.592357e-02 1.798753e-01 7.880845e-01 5 4
## 56 2.209157e-14 1.556716e-06 4.212482e-04 9.999913e-01 5 5
## 57 1.338923e-12 2.567915e-05 2.024604e-02 9.997242e-01 5 2
## 58 4.051742e-15 4.540827e-07 1.227409e-02 9.999153e-01 5 5
## 59 3.150353e-15 7.204367e-06 4.859033e-03 9.999672e-01 5 4
## 60 4.790125e-12 5.275800e-01 8.881432e-01 9.018543e-03 4 4
## 61 3.478491e-12 3.696410e-05 1.670440e-02 9.996327e-01 5 5
## 62 4.461375e-15 4.833489e-05 3.720675e-04 9.999799e-01 5 5
## 63 1.682527e-09 7.343137e-03 6.247346e-01 7.465260e-01 5 4
## 64 1.962579e-16 6.971686e-06 2.988786e-01 9.972831e-01 5 5
## 65 4.305177e-17 3.956724e-07 2.299732e-03 9.999882e-01 5 5
## 66 4.258768e-15 3.466689e-09 1.716923e-03 9.999970e-01 5 5
## 67 2.391509e-10 4.033100e-06 4.878184e-02 9.850915e-01 5 3
## 68 2.858808e-10 2.025178e-02 8.656206e-02 9.222738e-01 5 3
## 69 4.190193e-14 1.273283e-03 2.262612e-01 8.761264e-01 5 4
## 70 7.052863e-18 3.037610e-07 1.608623e-01 9.949423e-01 5 4
## 71 4.831165e-14 3.431154e-07 4.730903e-03 9.999758e-01 5 5
## 72 8.358503e-16 4.579132e-08 5.806680e-02 9.998100e-01 5 5
## 73 4.154822e-09 5.727670e-03 1.089057e-01 8.621289e-01 5 2
## 74 7.575056e-09 3.462161e-03 1.090078e-01 8.847115e-01 5 4
## 75 2.570830e-14 4.349692e-06 4.285951e-01 9.939393e-01 5 4
## 76 8.864718e-16 4.281514e-07 4.829712e-01 7.442453e-01 5 5
## 77 2.611004e-19 2.005940e-05 2.870992e-04 6.127295e-02 5 3
## 78 3.571751e-20 1.377117e-04 4.036341e-01 5.356998e-01 5 4
## 79 1.144336e-18 1.978554e-01 3.730323e-01 2.061436e-09 4 5
## 80 5.231239e-15 3.579392e-09 3.332599e-04 9.999969e-01 5 5
## 81 2.289978e-18 1.658204e-07 2.120291e-01 9.861371e-01 5 5
## 82 8.106142e-20 5.624745e-05 1.667267e-02 9.982126e-01 5 4
## 83 2.273743e-21 4.334428e-08 1.309048e-03 4.050206e-06 4 5
## 84 1.374814e-13 5.636753e-06 4.191744e-03 9.931778e-01 5 5
## 85 3.462627e-16 1.167015e-03 2.051689e-01 7.980475e-01 5 5
## 86 4.789217e-17 2.133182e-05 8.804033e-02 4.535790e-07 4 3
## 87 5.713312e-03 3.132172e-04 9.857762e-01 1.192498e-08 4 4
## 88 7.104636e-14 6.548901e-04 1.003657e-03 1.701211e-03 5 5
## 89 2.223932e-22 2.928979e-08 9.185538e-03 9.999681e-01 5 5
## 90 1.144469e-13 1.429384e-06 9.189796e-01 9.630924e-03 4 3
## 91 1.694464e-16 4.209144e-01 7.736872e-02 8.450676e-01 5 3
## 92 2.145726e-16 7.807843e-03 3.385508e-01 2.573447e-01 4 5
## 93 5.569877e-23 7.773169e-07 9.992711e-01 3.400780e-04 4 4
## 94 1.634966e-14 3.532502e-16 9.520513e-04 1.074804e-03 5 5
## 95 2.637427e-12 5.867656e-05 4.534966e-05 9.979791e-01 5 5
## 96 8.777452e-25 4.140343e-07 4.308759e-01 9.963121e-01 5 5
## 97 6.912250e-20 8.183848e-05 8.710642e-03 9.985078e-01 5 5
## 98 5.633976e-18 3.526939e-06 1.324341e-03 9.987287e-01 5 5
## 99 6.105218e-03 7.687453e-04 1.006467e-03 1.880022e-01 5 2
## 100 9.317851e-14 2.212055e-03 6.546561e-01 2.769063e-01 4 5CM <- table(Evaluation$Actual,Evaluation$Vote)
CM##
## 3 4 5
## 2 0 2 5
## 3 7 9 9
## 4 5 26 31
## 5 2 14 98#Proportions
Overall <- length(Evaluation$Actual)
Length2 <- length(which(Evaluation$Actual==2))
Length3 <- length(which(Evaluation$Actual==3))
Length4 <- length(which(Evaluation$Actual==4))
Length5 <- length(which(Evaluation$Actual==5))
#TP
TP <- sum(7,26,98)
#Accuracy
Accuracy <- TP/sum(CM)
#Precision
Rows <- rowSums(CM)
Precision2 <- CM[2,1]/Rows[2]
Precision3 <- CM[3,2]/Rows[3]
Precision4 <- CM[4,3]/Rows[4]
Precision <- (Precision2*Length3+Precision3*Length4+Precision4*Length5)/208
#Recall
Col <- colSums(CM)
Recall2 <- CM[2,1]/Col[1]
Recall3 <- CM[3,2]/Col[2]
Recall4 <- CM[4,3]/Col[3]
Recall <- (Recall2*Length3+Recall3*Length4+Recall4*Length5)/208
Accuracy## [1] 0.6298077Precision## 3
## 0.6298077Recall## 3
## 0.5876621