setwd("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TFIDF/10")#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$Score#Import Features
Features <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TFIDF/10/Feature Set 1: 10th TFIDF.csv")
Features <- Features[-1]#Class 2
Label2 <- list()
for(i in 1:1000){
if(Label[i]==3| Label[i]==4){
Label2[i] <- 1
}else{
Label2[i] <- 0
}
}#As Factor
Label2 <- as.factor(unlist(Label2))#Class 3
Label3 <- list()
for(i in 1:1000){
if(Label[i]==5| Label[i]==6){
Label3[i] <- 1
}else{
Label3[i] <- 0
}
}#As Factor
Label3 <- as.factor(unlist(Label3))#Class 4
Label4 <- list()
for(i in 1:1000){
if(Label[i]==7| Label[i]==8){
Label4[i] <- 1
}else{
Label4[i] <- 0
}
}#As Factor
Label4 <- as.factor(unlist(Label4))#Class 5
Label5 <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
Label5[i] <- 1
}else{
Label5[i] <- 0
}
}#As Factor
Label5 <- as.factor(unlist(Label5))#All Labels
All <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
All[i] <- 5
}else if(Label[i]==7| Label[i]==8){
All[i] <- 4
}else if(Label[i]==5| Label[i]==6){
All[i] <- 3
}else{
All[i] <- 2
}
}#As Factor
All <- as.factor(unlist(All))#Control
Control.df <- data.frame(matrix(seq(1,1000),ncol=1,nrow=1000))
Control.df$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 1:2396){
Features[,i] <- as.factor(Features[,i])
}
str(Features)## 'data.frame': 1000 obs. of 2396 variables:
## $ abit : Factor w/ 3 levels "0","0.199239650770269",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ abl : Factor w/ 13 levels "0","0.0350594603513238",..: 2 1 1 1 1 1 1 1 1 1 ...
## $ about : Factor w/ 2 levels "0","0.71184173461872": 1 1 1 1 1 1 1 1 1 1 ...
## $ abov : Factor w/ 2 levels "0","0.398631371386483": 1 1 1 1 1 1 1 1 1 1 ...
## $ absolut : Factor w/ 10 levels "0","0.0363483389163396",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ accent : Factor w/ 3 levels "0","0.373574345194254",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ accept : Factor w/ 4 levels "0","0.133028917205412",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ access : Factor w/ 29 levels "0","0.0374430565263987",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ accid : Factor w/ 2 levels "0","0.293111302490061": 1 1 1 1 1 1 1 1 1 1 ...
## $ accommod : Factor w/ 15 levels "0","0.0542344025878372",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ accomplish : Factor w/ 2 levels "0","0.586222604980123": 1 1 1 1 1 1 1 1 1 1 ...
## $ accur : Factor w/ 2 levels "0","0.369103121654151": 1 1 1 1 1 1 1 1 1 1 ...
## $ acess : Factor w/ 2 levels "0","0.35592086730936": 1 1 1 1 1 1 1 1 1 1 ...
## $ ach : Factor w/ 3 levels "0","0.199239650770269",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ acknowledg : Factor w/ 4 levels "0","0.0468202334298376",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ acomod : Factor w/ 2 levels "0","0.199315685693242": 1 1 1 1 1 1 1 1 1 1 ...
## $ across : Factor w/ 4 levels "0","0.100973756433023",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ activ : Factor w/ 2 levels "0","0.553654682481227": 1 1 1 1 1 1 1 1 1 1 ...
## $ actual : Factor w/ 10 levels "0","0.0365047043394216",..: 1 1 1 1 1 1 1 1 10 1 ...
## $ adaptor : Factor w/ 2 levels "0","0.284736693847488": 1 1 1 1 1 1 1 1 1 1 ...
## $ add : Factor w/ 3 levels "0","0.218677665479563",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ addit : Factor w/ 4 levels "0","0.0941665368982127",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ adequ : Factor w/ 7 levels "0","0.127829095760794",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ adjac : Factor w/ 5 levels "0","0.0926253986588615",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ adjust : Factor w/ 2 levels "0","0.321476912408454": 1 1 1 1 1 1 1 1 1 1 ...
## $ ador : Factor w/ 2 levels "0","0.71184173461872": 1 1 1 1 1 1 1 1 1 1 ...
## $ adult : Factor w/ 4 levels "0","0.152378577889835",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ advanc : Factor w/ 7 levels "0","0.0441965376283888",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ advantag : Factor w/ 2 levels "0","0.321476912408454": 1 1 1 1 1 1 1 1 1 1 ...
## $ adverti : Factor w/ 8 levels "0","0.0522513092160911",..: 1 1 1 1 3 1 1 1 1 1 ...
## $ advi : Factor w/ 4 levels "0","0.0805848248455859",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ advic : Factor w/ 7 levels "0","0.0709694402302013",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ affect : Factor w/ 6 levels "0","0.0920946528888521",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ afford : Factor w/ 3 levels "0","0.0896578428466209",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ afraid : Factor w/ 3 levels "0","0.075342725081194",..: 1 1 1 2 1 1 1 1 1 1 ...
## $ africa : Factor w/ 2 levels "0","0.383299395563926": 1 1 1 1 1 1 1 1 1 1 ...
## $ after : Factor w/ 6 levels "0","0.112409649849628",..: 1 1 1 1 2 1 1 1 1 1 ...
## $ afternoon : Factor w/ 3 levels "0","0.224144607116552",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ afterward : Factor w/ 2 levels "0","0.498289214233104": 1 1 1 1 1 1 1 1 1 1 ...
## $ age : Factor w/ 2 levels "0","0.332192809488736": 1 1 1 1 1 1 1 1 1 1 ...
## $ ago : Factor w/ 3 levels "0","0.25616526527606",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ agr : Factor w/ 2 levels "0","0.150996731585789": 1 1 1 1 1 1 1 1 1 1 ...
## $ agreeabl : Factor w/ 2 levels "0","0.498289214233104": 1 1 1 1 1 1 1 1 1 1 ...
## $ ahead : Factor w/ 2 levels "0","0.664385618977473": 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 ...
## $ aircon : Factor w/ 3 levels "0","0.320206581595075",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ aircondit : Factor w/ 5 levels "0","0.177017428548046",..: 1 1 1 1 1 1 1 1 1 3 ...
## $ airi : Factor w/ 4 levels "0","0.0805848248455859",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ airless : Factor w/ 2 levels "0","0.369103121654151": 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 ...
## $ alarm : Factor w/ 4 levels "0","0.209520544598523",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ albeit : Factor w/ 2 levels "0","0.181196077902947": 1 1 1 1 1 1 1 1 1 1 ...
## $ albert : Factor w/ 3 levels "0","0.119543790462161",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ albrt : Factor w/ 2 levels "0","0.586222604980123": 1 1 1 1 1 1 1 1 1 1 ...
## $ alcohol : Factor w/ 2 levels "0","0.41524101186092": 1 1 1 1 1 1 1 1 1 1 ...
## $ aldo : Factor w/ 2 levels "0","0.766598791127853": 1 1 1 1 1 1 1 1 1 1 ...
## $ alittl : Factor w/ 2 levels "0","0.905980389514735": 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 ...
## $ allevi : Factor w/ 2 levels "0","0.163373512863313": 1 1 1 1 1 1 1 1 1 1 ...
## $ alloc : Factor w/ 5 levels "0","0.26552614282207",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ allow : Factor w/ 11 levels "0","0.0962877708663004",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ almost : Factor w/ 3 levels "0","0.0896578428466209",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ along : Factor w/ 3 levels "0","0.135845216434274",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ alot : Factor w/ 2 levels "0","0.20762050593046": 1 1 1 1 1 1 1 1 1 1 ...
## $ alreadi : Factor w/ 7 levels "0","0.0620237124700919",..: 1 1 1 2 1 1 1 1 1 1 ...
## $ alright : Factor w/ 2 levels "0","0.171823866976933": 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 ...
## $ altern : Factor w/ 4 levels "0","0.126982148241529",..: 1 1 1 1 1 1 1 1 1 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 ...
## $ ambianc : Factor w/ 2 levels "0","0.996578428466209": 1 1 1 1 1 1 1 1 1 1 ...
## $ ambienc : Factor w/ 3 levels "0","0.249049563462836",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ amen : Factor w/ 9 levels "0","0.0703614574208292",..: 1 3 1 1 1 1 1 1 1 1 ...
## $ amend : Factor w/ 2 levels "0","0.47456115641248": 1 1 1 1 1 1 1 1 1 1 ...
## $ american : Factor w/ 4 levels "0","0.074828765928044",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ amount : Factor w/ 3 levels "0","0.0536873310458808",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ amsterdam : Factor w/ 22 levels "0","0.0329721716528431",..: 1 6 1 4 1 1 1 1 1 1 ...
## $ and : Factor w/ 18 levels "0","0.0310101988449161",..: 2 1 1 1 1 1 1 1 1 1 ...
## $ angl : Factor w/ 2 levels "0","0.216647484449176": 1 1 1 1 1 1 1 1 1 1 ...
## $ ann : Factor w/ 2 levels "0","0.553654682481227": 1 1 1 1 1 1 1 1 1 1 ...
## $ anna : Factor w/ 3 levels "0","0.640413163190149",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ annex : Factor w/ 2 levels "0","0.181196077902947": 1 1 1 1 1 1 1 1 1 1 ...
## $ announc : Factor w/ 2 levels "0","0.191649697781963": 1 1 1 1 1 1 1 1 1 1 ...
## $ annoy : Factor w/ 10 levels "0","0.0357491904726637",..: 2 1 1 1 1 1 1 1 1 1 ...
## $ anoth : Factor w/ 15 levels "0","0.0368768225305658",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ ansterdam : Factor w/ 2 levels "0","0.163373512863313": 1 1 1 1 1 1 1 1 1 1 ...
## $ answer : Factor w/ 7 levels "0","0.054672753955118",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ ant : Factor w/ 2 levels "0","1.99315685693242": 1 1 1 1 1 1 1 1 1 1 ...
## $ anymor : Factor w/ 6 levels "0","0.0419992098339271",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ anyon : Factor w/ 11 levels "0","0.067109658482573",..: 1 5 1 1 1 1 1 1 1 1 ...
## $ anyth : Factor w/ 14 levels "0","0.0368768225305658",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ anyway : Factor w/ 6 levels "0","0.0457715939507469",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ anywh : Factor w/ 4 levels "0","0.0838082178394093",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ apart : Factor w/ 9 levels "0","0.0557262742772967",..: 1 1 1 1 1 1 1 7 1 1 ...
## $ apex : Factor w/ 2 levels "0","0.203383352748206": 1 1 1 1 1 1 1 1 1 1 ...
## $ apolog : Factor w/ 4 levels "0","0.0997716879040587",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ appal : Factor w/ 3 levels "0","0.640413163190149",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ appar : Factor w/ 3 levels "0","0.169165741220039",..: 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
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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 1 8.676202e-12 0.9999061 9.391955e-05 0.0009227284 0.999077272
## 2 1 1.203093e-09 0.9994303 5.697425e-04 0.9666246326 0.033375367
## 3 1 1.313707e-11 0.9963059 3.694125e-03 0.9887541607 0.011245839
## 4 1 3.869451e-12 0.2152280 7.847720e-01 0.5646777162 0.435322284
## 5 1 6.351230e-10 0.9999985 1.471869e-06 0.9945943335 0.005405667
## 6 1 5.117065e-14 0.9999996 3.921571e-07 0.9981965049 0.001803495
## Class 5: 0 Class5: 1
## 1 6.904764e-01 0.3095236
## 2 9.071920e-02 0.9092808
## 3 9.938362e-04 0.9990062
## 4 7.296700e-01 0.2703300
## 5 2.276723e-04 0.9997723
## 6 1.127997e-06 0.9999989Transformed.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 8.676202e-12 9.391955e-05 0.999077272 0.3095236
## 2 1.203093e-09 5.697425e-04 0.033375367 0.9092808
## 3 1.313707e-11 3.694125e-03 0.011245839 0.9990062
## 4 3.869451e-12 7.847720e-01 0.435322284 0.2703300
## 5 6.351230e-10 1.471869e-06 0.005405667 0.9997723
## 6 5.117065e-14 3.921571e-07 0.001803495 0.9999989Evaluation <- 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 8.676202e-12 9.391955e-05 9.990773e-01 0.309523649 4 4
## 2 1.203093e-09 5.697425e-04 3.337537e-02 0.909280801 5 5
## 3 1.313707e-11 3.694125e-03 1.124584e-02 0.999006164 5 5
## 4 3.869451e-12 7.847720e-01 4.353223e-01 0.270330036 3 4
## 5 6.351230e-10 1.471869e-06 5.405667e-03 0.999772328 5 4
## 6 5.117065e-14 3.921571e-07 1.803495e-03 0.999998872 5 4
## 7 3.190159e-10 9.619174e-04 9.947708e-02 0.996037399 5 5
## 8 5.423927e-11 7.320322e-04 1.180217e-01 0.996411145 5 3
## 9 2.214680e-16 1.800219e-08 1.929954e-03 0.999999491 5 5
## 10 1.065984e-10 8.646170e-04 7.072121e-02 0.997634968 5 3
## 11 5.667426e-14 9.845672e-07 4.227896e-02 0.999890076 5 4
## 12 2.522543e-14 8.345028e-02 8.942315e-04 0.998578262 5 3
## 13 1.596546e-12 2.663140e-07 4.017636e-03 0.999989319 5 5
## 14 2.217741e-12 5.158214e-03 1.764989e-02 0.998471738 5 4
## 15 2.941318e-13 7.276332e-05 9.301738e-01 0.931125617 5 4
## 16 1.204301e-14 9.564039e-01 1.706593e-06 0.999243534 5 5
## 17 9.946473e-17 5.625676e-09 3.105547e-03 0.999997813 5 4
## 18 1.893770e-12 3.684857e-05 9.780251e-03 0.999925505 5 5
## 19 3.812310e-14 2.980716e-04 6.360569e-01 0.944268231 5 4
## 20 2.185146e-15 3.942539e-08 1.632847e-01 0.999898479 5 2
## 21 1.631182e-13 7.225201e-01 9.348081e-01 0.014953521 4 4
## 22 1.112200e-14 2.147408e-04 9.385137e-01 0.816534603 4 4
## 23 4.546608e-11 1.588310e-04 1.632468e-01 0.997223763 5 3
## 24 1.551716e-16 4.868950e-02 4.330922e-02 0.980018114 5 5
## 25 8.220387e-14 2.290938e-06 8.145784e-04 0.999967320 5 4
## 26 2.287806e-14 3.964191e-06 9.651443e-06 0.999999919 5 5
## 27 9.954643e-17 1.489932e-06 3.184692e-04 0.999994747 5 4
## 28 1.176499e-12 1.932503e-04 4.510742e-01 0.993310170 5 5
## 29 9.292842e-15 2.247665e-03 9.694122e-01 0.304524251 4 3
## 30 5.657076e-10 1.553440e-03 1.157345e-01 0.994063380 5 3
## 31 4.960204e-17 2.936033e-07 1.690256e-06 0.999999954 5 5
## 32 1.137303e-16 1.216684e-09 9.287956e-01 0.998298630 5 5
## 33 3.265468e-16 9.852732e-03 1.597382e-01 0.973840683 5 5
## 34 7.768603e-11 1.918118e-03 1.673354e-01 0.992449116 5 3
## 35 7.859916e-12 1.295114e-05 1.405890e-01 0.990406881 5 4
## 36 6.125298e-14 7.950514e-04 1.202840e-03 0.999763483 5 4
## 37 4.963421e-11 2.208852e-04 4.076696e-01 0.992368057 5 5
## 38 6.188716e-14 1.711443e-04 7.394886e-01 0.985477459 5 3
## 39 2.471335e-16 1.725662e-08 6.018308e-07 0.999999999 5 5
## 40 9.480203e-12 1.176351e-05 8.264750e-02 0.997768541 5 3
## 41 6.151776e-17 1.044972e-09 6.308179e-09 1.000000000 5 5
## 42 4.209287e-13 8.812699e-01 6.241611e-02 0.629805677 3 4
## 43 5.449696e-11 1.789844e-04 9.931665e-01 0.569376178 4 5
## 44 1.843851e-14 1.761054e-04 9.999674e-01 0.004421912 4 3
## 45 8.943985e-14 5.079945e-04 1.222923e-06 0.999983308 5 5
## 46 5.855868e-13 1.293802e-04 8.135405e-03 0.999746264 5 4
## 47 2.741568e-12 3.504416e-03 8.426954e-03 0.994707065 5 5
## 48 1.850219e-14 7.503094e-09 1.038797e-02 0.999986406 5 3
## 49 9.163059e-18 8.343676e-09 2.529059e-06 0.999999947 5 5
## 50 1.310257e-14 1.598402e-04 2.756814e-02 0.999522484 5 5
## 51 3.677139e-13 8.113590e-06 5.722211e-03 0.999951596 5 4
## 52 4.918097e-14 7.411154e-07 6.173445e-02 0.999918144 5 5
## 53 1.341986e-12 2.869269e-05 7.340764e-01 0.993511225 5 4
## 54 1.284512e-11 1.746888e-04 4.290020e-01 0.993376854 5 4
## 55 5.811643e-12 1.089171e-03 1.837772e-03 0.999889574 5 4
## 56 7.419313e-12 1.042043e-05 1.130614e-02 0.999176215 5 5
## 57 2.434935e-12 5.312471e-05 3.393601e-02 0.999621942 5 2
## 58 6.538434e-17 7.671436e-09 1.365704e-01 0.999899182 5 5
## 59 5.435994e-13 4.765922e-06 8.852500e-03 0.999838734 5 4
## 60 2.262822e-12 1.699183e-04 5.468478e-01 0.983915717 5 4
## 61 8.321301e-14 7.809485e-04 4.824356e-04 0.999926472 5 5
## 62 2.401182e-14 1.557966e-03 6.731363e-04 0.999889227 5 5
## 63 1.932828e-14 3.183750e-06 5.868054e-02 0.999768671 5 4
## 64 2.863585e-16 2.345279e-05 8.475688e-02 0.998077613 5 5
## 65 3.716267e-18 9.589778e-09 3.523234e-07 0.999999998 5 5
## 66 5.464305e-12 3.421204e-08 5.673278e-05 0.999998505 5 5
## 67 5.271397e-14 1.199793e-04 3.336015e-03 0.999937860 5 3
## 68 2.029324e-12 5.515777e-04 7.076808e-02 0.995786544 5 3
## 69 1.044893e-16 3.515477e-06 8.439259e-01 0.990986670 5 4
## 70 4.800449e-13 6.084361e-04 2.330794e-02 0.999027995 5 4
## 71 1.231028e-13 3.203905e-07 2.276844e-02 0.999818914 5 5
## 72 3.052761e-15 1.143671e-09 6.226369e-04 0.999999333 5 5
## 73 2.866246e-10 8.408950e-05 2.248163e-01 0.988996782 5 2
## 74 2.594845e-10 9.032051e-05 2.089692e-01 0.989681854 5 4
## 75 3.034782e-10 5.621804e-06 9.418721e-02 0.901760559 5 4
## 76 8.970794e-15 3.819240e-03 9.538244e-01 0.176384048 4 5
## 77 3.665166e-15 5.549350e-07 9.999988e-01 0.006651782 4 3
## 78 4.521079e-13 7.181885e-08 3.100828e-01 0.998703133 5 4
## 79 8.880919e-11 7.334721e-04 8.686732e-02 0.997054511 5 5
## 80 1.154603e-19 2.765719e-08 8.835123e-02 0.999898988 5 5
## 81 6.139038e-16 1.163066e-07 2.228855e-03 0.999998459 5 5
## 82 6.285701e-16 1.406258e-08 2.217798e-06 0.999999979 5 4
## 83 9.957784e-01 5.418226e-05 9.664081e-04 0.075301756 2 5
## 84 6.335946e-19 5.885264e-09 9.275849e-05 0.999999953 5 5
## 85 5.646235e-16 3.705247e-06 2.049117e-05 0.999999660 5 5
## 86 1.842995e-03 5.237766e-02 6.140164e-04 0.061441477 5 3
## 87 1.374689e-14 1.229049e-03 1.212856e-06 0.999997654 5 4
## 88 3.109605e-11 1.233590e-04 9.981229e-01 0.350201554 4 5
## 89 6.596336e-18 1.423172e-08 9.999960e-01 0.109038596 4 5
## 90 2.767446e-14 4.630962e-07 2.417654e-04 0.999999220 5 3
## 91 1.098664e-13 4.878078e-03 8.886686e-03 0.987965101 5 3
## 92 2.406708e-15 9.902475e-05 9.995675e-01 0.009090852 4 5
## 93 1.952708e-13 4.523985e-06 9.961801e-01 0.703741702 4 4
## 94 2.894780e-12 1.276629e-05 9.944750e-01 0.519309180 4 5
## 95 8.428023e-14 3.133929e-04 1.519635e-02 0.999544154 5 5
## 96 1.135673e-13 9.877995e-06 9.996749e-01 0.117713707 4 5
## 97 1.288343e-13 2.182782e-05 2.145045e-02 0.999874487 5 5
## 98 1.060526e-17 3.658185e-02 3.936779e-01 0.851204546 5 5
## 99 2.749324e-09 7.603557e-06 3.938168e-05 0.999915391 5 2
## 100 1.550629e-13 9.922438e-04 1.612771e-01 0.993141047 5 5CM <- table(Evaluation$Actual,Evaluation$Vote)
CM##
## 2 3 4 5
## 2 0 0 1 6
## 3 0 1 3 21
## 4 0 2 11 49
## 5 1 0 12 101#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] 0.5432692Precision## 2
## 0.5432692Recall## 2
## 0.4742473