setwd("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TFIDF/90")#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$Score#Import Features
Features <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TFIDF/90/Feature Set 1 90th TFIDF.csv")
Features <- Features[-1]#Class 2
Label2 <- list()
for(i in 1:1000){
if(Label[i]==3| Label[i]==4){
Label2[i] <- 1
}else{
Label2[i] <- 0
}
}#As Factor
Label2 <- as.factor(unlist(Label2))#Class 3
Label3 <- list()
for(i in 1:1000){
if(Label[i]==5| Label[i]==6){
Label3[i] <- 1
}else{
Label3[i] <- 0
}
}#As Factor
Label3 <- as.factor(unlist(Label3))#Class 4
Label4 <- list()
for(i in 1:1000){
if(Label[i]==7| Label[i]==8){
Label4[i] <- 1
}else{
Label4[i] <- 0
}
}#As Factor
Label4 <- as.factor(unlist(Label4))#Class 5
Label5 <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
Label5[i] <- 1
}else{
Label5[i] <- 0
}
}#As Factor
Label5 <- as.factor(unlist(Label5))#All Labels
All <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
All[i] <- 5
}else if(Label[i]==7| Label[i]==8){
All[i] <- 4
}else if(Label[i]==5| Label[i]==6){
All[i] <- 3
}else{
All[i] <- 2
}
}#As Factor
All <- as.factor(unlist(All))#Control
Control.df <- data.frame(matrix(seq(1,1000),ncol=1,nrow=1000))
Control.df$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 1:268){
Features[,i] <- as.factor(Features[,i])
}
str(Features)## 'data.frame': 1000 obs. of 268 variables:
## $ access : Factor w/ 29 levels "0","0.0374430565263987",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ air : Factor w/ 23 levels "0","0.0694786237666482",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ airport : Factor w/ 18 levels "0","0.0323791021408926",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ all : Factor w/ 15 levels "0","0.0441909947012006",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ also : Factor w/ 56 levels "0","0.0203241634283911",..: 1 1 1 31 1 1 41 1 50 1 ...
## $ although : Factor w/ 22 levels "0","0.0505889368905357",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ alway : Factor w/ 19 levels "0","0.0418063421464794",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ amaz : Factor w/ 32 levels "0","0.0329053505822598",..: 1 1 1 1 7 1 1 1 1 1 ...
## $ anyth : Factor w/ 14 levels "0","0.0368768225305658",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ area : Factor w/ 61 levels "0","0.0271674294106491",..: 1 1 1 3 1 1 1 1 1 1 ...
## $ around : Factor w/ 41 levels "0","0.0246435499049411",..: 1 1 1 4 1 1 1 1 1 1 ...
## $ arriv : Factor w/ 44 levels "0","0.0239070024205872",..: 2 1 1 1 13 1 1 1 1 1 ...
## $ ask : Factor w/ 36 levels "0","0.0239070024205872",..: 2 1 22 1 1 1 1 1 1 1 ...
## $ attent : Factor w/ 13 levels "0","0.0634889625010771",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ away : Factor w/ 25 levels "0","0.0677544644918298",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ awesom : Factor w/ 8 levels "0","0.0715842936260448",..: 1 1 1 1 1 4 1 1 1 1 ...
## $ back : Factor w/ 34 levels "0","0.0249424149997802",..: 1 8 19 1 1 1 1 1 1 1 ...
## $ bad : Factor w/ 29 levels "0","0.0272845290366049",..: 1 1 1 5 1 1 1 1 1 1 ...
## $ bar : Factor w/ 54 levels "0","0.0290587365080957",..: 1 1 27 2 1 1 1 1 1 1 ...
## $ basement : Factor w/ 7 levels "0","0.238091025288417",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ bath : Factor w/ 28 levels "0","0.0492759741758762",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ bathroom : Factor w/ 77 levels "0","0.015144675379303",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ beauti : Factor w/ 49 levels "0","0.0197293673259075",..: 2 1 1 1 1 1 29 1 1 1 ...
## $ bed : Factor w/ 81 levels "0","0.0118685346307338",..: 1 1 1 20 1 1 52 1 1 47 ...
## $ bedroom : Factor w/ 24 levels "0","0.0318678329035171",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ best : Factor w/ 18 levels "0","0.0310101988449161",..: 2 1 1 1 1 1 1 1 1 1 ...
## $ better : Factor w/ 31 levels "0","0.0400927037942017",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ big : Factor w/ 37 levels "0","0.0264729656114344",..: 5 1 1 1 1 1 1 1 1 32 ...
## $ birthday : Factor w/ 10 levels "0","0.0999391071061732",..: 1 1 1 1 2 1 1 1 1 1 ...
## $ bit : Factor w/ 42 levels "0","0.023351467629966",..: 1 21 20 1 1 1 1 1 1 1 ...
## $ book : Factor w/ 50 levels "0","0.0320927037942017",..: 24 1 1 1 34 1 1 1 1 1 ...
## $ breakfast : Factor w/ 90 levels "0","0.0157034504121546",..: 1 1 42 1 1 1 1 64 1 43 ...
## $ brilliant : Factor w/ 13 levels "0","0.108023182181396",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ broken : Factor w/ 11 levels "0","0.0365047043394216",..: 2 1 1 1 1 1 1 1 1 1 ...
## $ buffet : Factor w/ 26 levels "0","0.0632361711131696",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ build : Factor w/ 55 levels "0","0.040345447790058",..: 1 1 1 9 4 1 1 1 1 1 ...
## $ busi : Factor w/ 21 levels "0","0.0440508213281983",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ can : Factor w/ 46 levels "0","0.0230887948072477",..: 15 11 1 1 1 1 1 1 1 1 ...
## $ center : Factor w/ 20 levels "0","0.0310101988449161",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ centr : Factor w/ 27 levels "0","0.0502919736434201",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ central : Factor w/ 22 levels "0","0.077129392679527",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ chang : Factor w/ 30 levels "0","0.0277418116461569",..: 7 1 1 1 1 1 27 1 1 1 ...
## $ charg : Factor w/ 22 levels "0","0.0314906987863158",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ check : Factor w/ 38 levels "0","0.0474352912213136",..: 3 9 1 1 1 1 1 1 31 1 ...
## $ choic : Factor w/ 33 levels "0","0.053805492492745",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ citi : Factor w/ 33 levels "0","0.0251289388015567",..: 2 1 1 1 1 1 1 1 1 1 ...
## $ clean : Factor w/ 71 levels "0","0.0133006803572119",..: 1 1 1 15 1 1 45 1 55 1 ...
## $ close : Factor w/ 49 levels "0","0.0187428320493651",..: 2 1 1 1 1 1 1 1 1 1 ...
## $ coff : Factor w/ 33 levels "0","0.0363161562396799",..: 1 1 16 1 1 1 1 1 1 1 ...
## $ cold : Factor w/ 32 levels "0","0.047178567712185",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ comfi : Factor w/ 41 levels "0","0.0281174761719983",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ comfort : Factor w/ 74 levels "0","0.0229773826953868",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ complain : Factor w/ 15 levels "0","0.0509150730172569",..: 1 1 1 2 1 1 1 1 1 1 ...
## $ complet : Factor w/ 16 levels "0","0.0429322909868131",..: 1 1 1 1 7 1 1 1 1 1 ...
## $ condit : Factor w/ 16 levels "0","0.060373534200206",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ construct : Factor w/ 38 levels "0","0.0258798089514213",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ conveni : Factor w/ 27 levels "0","0.0435564414590335",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ cook : Factor w/ 11 levels "0","0.0638082178394093",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ cool : Factor w/ 18 levels "0","0.0640628374929118",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ couldn : Factor w/ 17 levels "0","0.0315299228479035",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ court : Factor w/ 47 levels "0","0.0208769027607051",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ day : Factor w/ 53 levels "0","0.0215460998093606",..: 20 1 1 5 1 1 1 1 1 1 ...
## $ decor : Factor w/ 28 levels "0","0.0455598906752292",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ definit : Factor w/ 26 levels "0","0.0305856484403264",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ delici : Factor w/ 13 levels "0","0.0728528437967558",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ design : Factor w/ 26 levels "0","0.027796119170624",..: 1 1 1 1 1 18 1 1 1 1 ...
## $ desk : Factor w/ 27 levels "0","0.0491134931686011",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ didn : Factor w/ 46 levels "0","0.0287283069882352",..: 1 1 1 1 7 1 27 1 1 1 ...
## $ difficult : Factor w/ 14 levels "0","0.0648255722379419",..: 1 1 9 1 1 1 1 1 1 1 ...
## $ don : Factor w/ 21 levels "0","0.0295649548568183",..: 2 1 1 1 1 1 1 1 1 1 ...
## $ door : Factor w/ 39 levels "0","0.0391316015277156",..: 1 1 1 10 1 1 1 1 1 1 ...
## $ doubl : Factor w/ 25 levels "0","0.0286313010027397",..: 2 1 1 1 1 1 1 1 1 1 ...
## $ drink : Factor w/ 37 levels "0","0.0371508495181978",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ due : Factor w/ 25 levels "0","0.0286313010027397",..: 2 1 1 1 1 1 1 1 1 1 ...
## $ earl : Factor w/ 45 levels "0","0.0210947470714082",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ easi : Factor w/ 36 levels "0","0.031338386442997",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ effici : Factor w/ 14 levels "0","0.0701856974666128",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ enough : Factor w/ 30 levels "0","0.0275361976608528",..: 1 1 1 1 1 1 1 1 1 18 ...
## $ especi : Factor w/ 20 levels "0","0.0398579391403032",..: 1 1 1 1 5 1 1 1 1 1 ...
## $ etc : Factor w/ 21 levels "0","0.0322024989858288",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ euro : Factor w/ 10 levels "0","0.0849482410402472",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ even : Factor w/ 54 levels "0","0.0211937353180435",..: 6 16 1 1 1 1 1 1 48 38 ...
## $ everyth : Factor w/ 34 levels "0","0.0427136762365872",..: 1 1 1 1 1 1 1 22 1 1 ...
## $ excel : Factor w/ 56 levels "0","0.0267857142857143",..: 1 8 1 1 1 1 1 1 1 1 ...
## $ except : Factor w/ 16 levels "0","0.0587832144341175",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ execut : Factor w/ 11 levels "0","0.120488012333792",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ expen : Factor w/ 30 levels "0","0.0453640074155625",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ experi : Factor w/ 19 levels "0","0.045742854169748",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ extra : Factor w/ 23 levels "0","0.0491134931686011",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ extrem : Factor w/ 25 levels "0","0.0308337087721812",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ facil : Factor w/ 29 levels "0","0.047655118901197",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ fantast : Factor w/ 31 levels "0","0.038692010141737",..: 1 6 1 1 1 1 1 1 1 1 ...
## $ far : Factor w/ 28 levels "0","0.0443373596844829",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ feel : Factor w/ 26 levels "0","0.0312029747455007",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ first : Factor w/ 35 levels "0","0.0255156933504106",..: 1 11 1 1 1 1 1 1 1 1 ...
## $ floor : Factor w/ 47 levels "0","0.0220416921663473",..: 2 1 1 23 1 1 31 1 1 1 ...
## $ food : Factor w/ 50 levels "0","0.029742854169748",..: 1 12 1 1 1 1 1 39 1 27 ...
## $ free : Factor w/ 30 levels "0","0.0277418116461569",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ fresh : Factor w/ 19 levels "0","0.0557294161848055",..: 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
## 8 4 0 0 1
## 9 4 0 0 1
## 10 5 0 0 0
## 11 3 0 1 0
## 12 3 0 1 0
## 13 5 0 0 0
## 14 3 0 1 0
## 15 4 0 0 1
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## 18 3 0 1 0
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## 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 0.9999702 2.981171e-05 0.9877781 0.0122219231 0.08321359 0.91678641
## 2 0.9977801 2.219888e-03 0.9793847 0.0206153289 0.90272066 0.09727934
## 3 0.9999798 2.024724e-05 0.8809822 0.1190177655 0.96770300 0.03229700
## 4 0.9999919 8.110091e-06 0.4958683 0.5041317022 0.11125941 0.88874059
## 5 0.9999627 3.734912e-05 0.9998959 0.0001041425 0.94960196 0.05039804
## 6 0.9999999 1.385146e-07 0.9999734 0.0000266480 0.98220210 0.01779790
## Class 5: 0 Class5: 1
## 1 0.6755768304 0.32442317
## 2 0.8722832524 0.12771675
## 3 0.0590669386 0.94093306
## 4 0.9575315434 0.04246846
## 5 0.0117238015 0.98827620
## 6 0.0003782457 0.99962175Transformed.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 2.981171e-05 0.0122219231 0.91678641 0.32442317
## 2 2.219888e-03 0.0206153289 0.09727934 0.12771675
## 3 2.024724e-05 0.1190177655 0.03229700 0.94093306
## 4 8.110091e-06 0.5041317022 0.88874059 0.04246846
## 5 3.734912e-05 0.0001041425 0.05039804 0.98827620
## 6 1.385146e-07 0.0000266480 0.01779790 0.99962175Evaluation <- 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 2.981171e-05 1.222192e-02 9.167864e-01 3.244232e-01 4 4
## 2 2.219888e-03 2.061533e-02 9.727934e-02 1.277167e-01 5 5
## 3 2.024724e-05 1.190178e-01 3.229700e-02 9.409331e-01 5 5
## 4 8.110091e-06 5.041317e-01 8.887406e-01 4.246846e-02 4 4
## 5 3.734912e-05 1.041425e-04 5.039804e-02 9.882762e-01 5 4
## 6 1.385146e-07 2.664800e-05 1.779790e-02 9.996218e-01 5 4
## 7 3.183280e-04 3.751837e-02 2.423392e-01 8.032873e-01 5 5
## 8 7.917882e-05 2.235396e-02 2.638803e-01 8.358610e-01 5 3
## 9 4.005905e-10 6.520461e-07 5.436243e-03 9.999683e-01 5 5
## 10 1.653272e-04 2.778736e-02 1.862006e-01 8.717613e-01 5 3
## 11 9.884965e-08 3.671033e-05 1.125014e-01 9.930723e-01 5 4
## 12 4.267634e-08 7.738726e-01 2.535460e-03 9.179118e-01 5 3
## 13 4.350407e-06 1.380916e-05 2.176088e-02 9.983414e-01 5 5
## 14 3.372574e-06 1.532696e-01 4.867125e-02 9.154552e-01 5 4
## 15 5.581654e-07 2.507978e-03 9.749483e-01 1.762206e-01 4 4
## 16 1.315219e-07 9.974822e-01 1.357160e-05 9.280691e-01 3 5
## 17 2.143660e-10 2.685905e-07 1.703674e-02 9.996776e-01 5 4
## 18 2.342534e-06 9.445182e-04 1.909776e-02 9.967114e-01 5 5
## 19 7.359682e-08 1.024516e-02 8.338056e-01 2.132323e-01 4 4
## 20 5.124251e-09 2.136392e-06 1.249092e-01 9.975857e-01 5 2
## 21 2.790528e-07 9.893239e-01 9.769410e-01 2.380233e-04 3 4
## 22 2.336716e-08 7.855924e-03 9.772346e-01 6.541407e-02 4 4
## 23 4.887354e-05 5.284141e-03 3.500891e-01 8.666679e-01 5 3
## 24 3.186954e-10 6.591050e-01 1.180192e-01 4.233840e-01 3 5
## 25 1.549610e-07 7.753596e-05 2.400963e-03 9.979375e-01 5 4
## 26 4.207896e-08 1.438329e-04 2.789299e-05 9.999948e-01 5 5
## 27 1.753115e-10 5.765168e-05 9.348271e-04 9.996547e-01 5 4
## 28 1.823602e-06 7.017464e-03 7.153877e-01 6.937312e-01 4 5
## 29 1.274325e-08 1.048190e-01 9.578068e-01 1.993730e-02 4 3
## 30 5.762887e-04 4.464789e-02 2.634466e-01 7.622982e-01 5 3
## 31 9.445214e-11 1.099514e-05 5.024030e-06 9.999970e-01 5 5
## 32 2.188927e-10 4.482248e-08 9.748825e-01 8.989396e-01 4 5
## 33 8.743993e-10 3.500729e-01 1.195998e-01 6.104436e-01 5 5
## 34 1.387169e-04 6.208893e-02 3.709064e-01 6.763770e-01 5 3
## 35 1.878018e-05 6.220211e-04 1.035008e-01 8.236088e-01 5 4
## 36 1.298377e-07 2.620293e-02 3.474049e-03 9.852914e-01 5 4
## 37 6.596214e-05 7.859384e-03 6.512419e-01 6.931814e-01 5 5
## 38 1.118997e-07 1.246074e-02 7.884430e-01 5.759855e-01 4 3
## 39 6.339964e-10 1.228086e-06 8.234833e-07 1.000000e+00 5 5
## 40 1.785894e-05 6.037890e-04 3.168673e-01 7.623630e-01 5 3
## 41 1.526122e-10 5.329002e-08 3.449795e-08 1.000000e+00 5 5
## 42 1.461258e-06 9.981483e-01 8.094368e-02 3.037472e-02 3 4
## 43 5.811372e-05 7.228224e-03 9.976073e-01 2.099150e-02 4 5
## 44 3.567286e-08 6.569374e-03 9.999887e-01 6.842881e-05 4 3
## 45 1.709870e-07 1.899782e-02 3.548174e-06 9.989104e-01 5 5
## 46 1.068674e-06 4.819695e-03 2.290046e-02 9.841028e-01 5 4
## 47 5.537296e-06 1.184251e-01 2.370891e-02 7.429594e-01 5 5
## 48 3.565685e-08 2.696712e-07 2.999444e-02 9.991202e-01 5 3
## 49 1.862303e-11 3.157010e-07 7.453680e-06 9.999965e-01 5 5
## 50 2.254463e-08 5.975008e-03 7.846556e-02 9.694037e-01 5 5
## 51 7.473423e-07 3.069035e-04 1.667883e-02 9.967877e-01 5 4
## 52 9.453065e-08 2.835730e-05 1.578672e-01 9.947497e-01 5 5
## 53 2.377984e-06 1.041943e-03 8.914749e-01 7.021612e-01 4 4
## 54 2.514930e-05 6.687885e-03 6.807302e-01 6.989693e-01 5 4
## 55 1.212676e-05 3.971510e-02 5.373025e-03 9.926964e-01 5 4
## 56 1.477023e-05 3.968802e-04 3.304944e-02 9.473616e-01 5 5
## 57 4.948745e-06 2.006162e-03 9.381765e-02 9.754395e-01 5 2
## 58 1.353646e-10 2.961462e-07 3.145294e-01 9.933533e-01 5 5
## 59 1.427786e-06 2.567121e-04 4.621468e-02 9.756241e-01 5 4
## 60 4.690384e-06 6.277594e-03 7.791937e-01 4.816256e-01 4 4
## 61 1.691224e-07 2.872259e-02 1.420514e-03 9.951283e-01 5 5
## 62 6.183388e-08 7.695754e-02 3.669533e-03 9.831130e-01 5 5
## 63 3.933428e-08 1.206210e-04 1.554510e-01 9.847945e-01 5 4
## 64 5.873605e-10 8.839410e-04 2.126038e-01 8.872745e-01 5 5
## 65 7.552953e-12 3.628499e-07 1.038377e-06 9.999998e-01 5 5
## 66 1.110555e-05 1.294485e-06 1.671861e-04 9.999005e-01 5 5
## 67 9.691485e-08 4.551147e-03 9.674932e-03 9.959554e-01 5 3
## 68 3.992489e-06 2.033711e-02 1.829180e-01 7.817226e-01 5 3
## 69 2.719181e-10 1.899580e-04 7.987208e-01 8.195686e-01 5 4
## 70 1.301317e-06 3.000139e-02 1.161407e-01 8.696458e-01 5 4
## 71 2.511795e-07 1.217433e-05 6.423219e-02 9.880635e-01 5 5
## 72 6.253402e-09 4.364402e-08 1.809918e-03 9.999559e-01 5 5
## 73 5.821977e-04 3.171883e-03 4.608427e-01 5.744835e-01 5 2
## 74 5.089167e-04 3.420645e-03 4.376600e-01 5.910667e-01 5 4
## 75 6.164099e-04 2.126691e-04 2.345702e-01 1.211703e-01 4 4
## 76 1.555711e-08 1.254700e-01 9.835566e-01 3.337311e-03 4 5
## 77 6.883785e-09 2.679253e-05 9.999998e-01 4.926538e-05 4 3
## 78 1.158607e-06 3.880361e-06 7.096902e-01 8.324940e-01 5 4
## 79 1.088257e-04 2.226479e-02 2.054945e-01 8.626749e-01 5 5
## 80 4.068024e-13 2.138682e-06 1.148730e-01 9.943405e-01 5 5
## 81 1.117834e-09 4.400039e-06 6.494521e-03 9.998995e-01 5 5
## 82 1.277508e-09 5.320880e-07 6.536332e-06 9.999986e-01 5 4
## 83 9.999999e-01 2.711498e-03 5.277006e-03 3.644003e-03 2 5
## 84 1.098370e-12 2.208922e-07 2.695569e-04 9.999970e-01 5 5
## 85 1.140453e-09 1.360553e-04 5.959845e-05 9.999779e-01 5 5
## 86 9.997232e-01 7.320419e-01 4.557957e-04 2.867895e-03 2 3
## 87 2.431716e-08 6.188008e-02 6.136841e-06 9.996754e-01 5 4
## 88 5.283300e-05 4.415244e-03 9.993587e-01 8.419558e-03 4 5
## 89 1.306312e-11 5.322027e-07 9.999986e-01 1.867495e-03 4 5
## 90 6.501604e-08 2.454343e-05 1.268884e-03 9.998849e-01 5 3
## 91 2.259393e-07 1.530361e-01 2.571641e-02 5.545834e-01 5 3
## 92 4.861334e-09 3.622847e-03 9.998504e-01 1.418401e-04 4 5
## 93 4.744516e-07 2.370722e-04 9.945114e-01 9.501911e-02 4 4
## 94 2.545084e-06 4.696510e-04 9.981133e-01 1.793716e-02 4 5
## 95 1.473909e-07 1.070846e-02 4.456116e-02 9.714634e-01 5 5
## 96 2.284881e-07 3.659144e-04 9.998869e-01 2.062461e-03 4 5
## 97 2.695165e-07 7.872738e-04 6.063792e-02 9.918366e-01 5 5
## 98 4.347245e-11 7.995534e-01 9.244554e-01 6.788205e-03 4 5
## 99 7.546332e-03 3.368786e-05 3.941187e-04 9.954958e-01 5 2
## 100 2.734350e-07 3.575855e-02 3.625786e-01 6.892207e-01 5 5CM <- table(Evaluation$Actual,Evaluation$Vote)
CM##
## 2 3 4 5
## 2 0 0 2 5
## 3 1 2 6 16
## 4 0 3 20 39
## 5 1 4 19 90#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))
#Accuracy
Accuracy <- sum(diag(CM))/sum(CM)
#Precision
Precision <- diag(CM)/rowSums(CM)
Precision <- (Precision[1]*Length2+Precision[2]*Length3+Precision[3]*Length4+Precision[4]*Length5)/208
#Recall
Recall <- diag(CM)/colSums(CM)
Recall <- (Recall[1]*Length2+Recall[2]*Length3+Recall[3]*Length4+Recall[4]*Length5)/208
Accuracy## [1] 0.5384615Precision## 2
## 0.5384615Recall## 2
## 0.4823968