setwd("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TFIDF/50")#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$Score#Import Features
Features <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TFIDF/50/Feature Set 1: 50th TFIDF.csv")
Features <- Features[-1]#Class 2
Label2 <- list()
for(i in 1:1000){
if(Label[i]==3| Label[i]==4){
Label2[i] <- 1
}else{
Label2[i] <- 0
}
}#As Factor
Label2 <- as.factor(unlist(Label2))#Class 3
Label3 <- list()
for(i in 1:1000){
if(Label[i]==5| Label[i]==6){
Label3[i] <- 1
}else{
Label3[i] <- 0
}
}#As Factor
Label3 <- as.factor(unlist(Label3))#Class 4
Label4 <- list()
for(i in 1:1000){
if(Label[i]==7| Label[i]==8){
Label4[i] <- 1
}else{
Label4[i] <- 0
}
}#As Factor
Label4 <- as.factor(unlist(Label4))#Class 5
Label5 <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
Label5[i] <- 1
}else{
Label5[i] <- 0
}
}#As Factor
Label5 <- as.factor(unlist(Label5))#All Labels
All <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
All[i] <- 5
}else if(Label[i]==7| Label[i]==8){
All[i] <- 4
}else if(Label[i]==5| Label[i]==6){
All[i] <- 3
}else{
All[i] <- 2
}
}#As Factor
All <- as.factor(unlist(All))#Control
Control.df <- data.frame(matrix(seq(1,1000),ncol=1,nrow=1000))
Control.df$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 1:1336){
Features[,i] <- as.factor(Features[,i])
}
str(Features)## 'data.frame': 1000 obs. of 1336 variables:
## $ 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 ...
## $ 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 ...
## $ 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 ...
## $ across : Factor w/ 4 levels "0","0.100973756433023",..: 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 ...
## $ 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 ...
## $ 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 ...
## $ 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 ...
## $ after : Factor w/ 6 levels "0","0.112409649849628",..: 1 1 1 1 2 1 1 1 1 1 ...
## $ ago : Factor w/ 3 levels "0","0.25616526527606",..: 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 ...
## $ 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 ...
## $ 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 ...
## $ 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 ...
## $ 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 ...
## $ alreadi : Factor w/ 7 levels "0","0.0620237124700919",..: 1 1 1 2 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 ...
## $ amen : Factor w/ 9 levels "0","0.0703614574208292",..: 1 3 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 ...
## $ anna : Factor w/ 3 levels "0","0.640413163190149",..: 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 ...
## $ 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 ...
## $ 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 ...
## $ appeal : Factor w/ 3 levels "0","0.249049563462836",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ appear : Factor w/ 6 levels "0","0.0955482023721841",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ applic : Factor w/ 2 levels "0","1.10730936496245": 1 1 1 1 1 1 1 1 1 1 ...
## $ appoint : Factor w/ 5 levels "0","0.221271785685058",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ appreci : Factor w/ 7 levels "0","0.0688310515635046",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ approach : Factor w/ 4 levels "0","0.174600453832103",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ april : Factor w/ 2 levels "0","0.830482023721841": 1 1 1 1 1 1 1 1 1 1 ...
## $ architectur : Factor w/ 7 levels "0","0.111830633090014",..: 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 ...
## $ arena : Factor w/ 11 levels "0","0.0472653465477106",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ aroom : Factor w/ 2 levels "0","0.71184173461872": 1 1 1 1 1 1 1 1 1 1 ...
## $ around : Factor w/ 41 levels "0","0.0246435499049411",..: 1 1 1 4 1 1 1 1 1 1 ...
## $ arrang : Factor w/ 4 levels "0","0.155200403406314",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ arriv : Factor w/ 44 levels "0","0.0239070024205872",..: 2 1 1 1 13 1 1 1 1 1 ...
## $ art : Factor w/ 3 levels "0","0.373574345194254",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ artwork : Factor w/ 2 levels "0","0.62286151779138": 1 1 1 1 1 1 1 1 1 1 ...
## $ ask : Factor w/ 36 levels "0","0.0239070024205872",..: 2 1 22 1 1 1 1 1 1 1 ...
## $ aspect : Factor w/ 6 levels "0","0.0682487159801315",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ athmosph : Factor w/ 2 levels "0","1.66096404744368": 1 1 1 1 1 1 1 1 1 1 ...
## $ atm : Factor w/ 5 levels "0","0.0711230739701972",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ atmosph : Factor w/ 12 levels "0","0.100097733323458",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ attend : Factor w/ 4 levels "0","0.178315357105126",..: 1 1 1 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 ...
## $ attic : Factor w/ 5 levels "0","0.0995723035582761",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ attitud : Factor w/ 4 levels "0","0.147031961121771",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ attract : Factor w/ 11 levels "0","0.147641248661661",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ atttent : Factor w/ 2 levels "0","1.24572303558276": 1 1 1 1 1 1 1 1 1 1 ...
## $ avail : Factor w/ 19 levels "0","0.0310101988449161",..: 2 1 1 1 1 1 1 1 1 1 ...
## $ averag : Factor w/ 7 levels "0","0.137662103127009",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ awar : Factor w/ 6 levels "0","0.107659946334855",..: 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 ...
## $ backyard : Factor w/ 3 levels "0","0.407535649302822",..: 1 1 1 1 1 2 1 1 1 1 ...
## $ bacon : Factor w/ 11 levels "0","0.0603986926343157",..: 1 1 1 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 ...
## $ bag : Factor w/ 9 levels "0","0.0669786950448278",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ bake : Factor w/ 4 levels "0","0.186240484087576",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ bang : Factor w/ 6 levels "0","0.0955482023721841",..: 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
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## 10 5 0 0 0
## 11 3 0 1 0
## 12 3 0 1 0
## 13 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 2.656601e-10 0.99958992 4.100755e-04 0.002914419 0.997085581
## 2 1 3.027672e-08 0.99826258 1.737416e-03 0.957187151 0.042812849
## 3 1 3.314706e-10 0.98891826 1.108174e-02 0.985427503 0.014572497
## 4 1 9.377100e-11 0.08231858 9.176814e-01 0.499875127 0.500124873
## 5 1 1.598334e-08 0.99999551 4.493665e-06 0.993008581 0.006991419
## 6 1 9.877070e-13 0.99999916 8.355598e-07 0.998728876 0.001271124
## Class 5: 0 Class5: 1
## 1 6.985307e-01 0.3014693
## 2 2.259405e-01 0.7740595
## 3 2.900733e-03 0.9970993
## 4 8.873229e-01 0.1126771
## 5 6.657917e-04 0.9993342
## 6 1.414472e-06 0.9999986Transformed.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 2.656601e-10 4.100755e-04 0.997085581 0.3014693
## 2 3.027672e-08 1.737416e-03 0.042812849 0.7740595
## 3 3.314706e-10 1.108174e-02 0.014572497 0.9970993
## 4 9.377100e-11 9.176814e-01 0.500124873 0.1126771
## 5 1.598334e-08 4.493665e-06 0.006991419 0.9993342
## 6 9.877070e-13 8.355598e-07 0.001271124 0.9999986Evaluation <- 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 2.656601e-10 4.100755e-04 9.970856e-01 0.301469256 4 4
## 2 3.027672e-08 1.737416e-03 4.281285e-02 0.774059481 5 5
## 3 3.314706e-10 1.108174e-02 1.457250e-02 0.997099267 5 5
## 4 9.377100e-11 9.176814e-01 5.001249e-01 0.112677092 3 4
## 5 1.598334e-08 4.493665e-06 6.991419e-03 0.999334208 5 4
## 6 9.877070e-13 8.355598e-07 1.271124e-03 0.999998586 5 4
## 7 7.513113e-09 2.930405e-03 1.263555e-01 0.988488685 5 5
## 8 1.366758e-09 2.234735e-03 1.472262e-01 0.989601005 5 3
## 9 5.573404e-15 5.496146e-08 2.498668e-03 0.999998511 5 5
## 10 2.593433e-09 2.650005e-03 8.985755e-02 0.993118303 5 3
## 11 1.426249e-12 3.005920e-06 5.409320e-02 0.999678472 5 4
## 12 6.113047e-13 2.177534e-01 1.160222e-03 0.995863327 5 3
## 13 4.017826e-11 8.130675e-07 5.198338e-03 0.999968753 5 5
## 14 5.374400e-11 1.560501e-02 2.278621e-02 0.995554293 5 4
## 15 7.411730e-12 2.195639e-04 9.453202e-01 0.822499700 4 4
## 16 3.038650e-13 9.853303e-01 2.205843e-06 0.997791077 5 5
## 17 2.506379e-15 1.697801e-08 4.026645e-03 0.999993620 5 4
## 18 3.177208e-11 7.499731e-05 8.457607e-03 0.999854713 5 5
## 19 9.593959e-13 9.094688e-04 6.936251e-01 0.852752176 5 4
## 20 5.499081e-14 1.203674e-07 2.017879e-01 0.999703045 5 2
## 21 3.968502e-12 8.875383e-01 9.486989e-01 0.005219445 4 4
## 22 2.802597e-13 6.562542e-04 9.519445e-01 0.602815995 4 4
## 23 1.067966e-09 4.770582e-04 2.019171e-01 0.992034981 5 3
## 24 3.905008e-15 1.351421e-01 5.539470e-02 0.943706541 5 5
## 25 2.068721e-12 6.994298e-06 1.054964e-03 0.999904396 5 4
## 26 5.757432e-13 1.210275e-05 1.250259e-05 0.999999762 5 5
## 27 2.511721e-15 4.561743e-06 4.091790e-04 0.999984718 5 4
## 28 2.960746e-11 5.897675e-04 5.156199e-01 0.980676989 5 5
## 29 2.174274e-13 6.781334e-03 9.762555e-01 0.131444133 4 3
## 30 1.387161e-08 4.672608e-03 1.450513e-01 0.982952014 5 3
## 31 1.248272e-15 8.963828e-07 2.189582e-06 0.999999866 5 5
## 32 2.862108e-15 3.714588e-09 9.441263e-01 0.995038684 5 5
## 33 8.217790e-15 2.948440e-02 1.976027e-01 0.927137942 5 5
## 34 1.960147e-09 5.774609e-03 2.071606e-01 0.978235158 5 3
## 35 1.978006e-10 3.953934e-05 1.748589e-01 0.972443186 5 4
## 36 1.541476e-12 2.423370e-03 1.557624e-03 0.999308356 5 4
## 37 1.250716e-09 6.750232e-04 4.703267e-01 0.978054807 5 5
## 38 1.505652e-12 5.118462e-04 7.864402e-01 0.959106663 5 3
## 39 6.219297e-15 5.268521e-08 7.796208e-07 0.999999998 5 5
## 40 2.309465e-10 3.570318e-05 1.042687e-01 0.993556081 5 3
## 41 1.548140e-15 3.190345e-09 8.171712e-09 1.000000000 5 5
## 42 1.063465e-11 9.579086e-01 7.893216e-02 0.368445357 3 4
## 43 1.382267e-09 5.509181e-04 9.947125e-01 0.310465718 4 5
## 44 4.646259e-13 5.312884e-04 9.999749e-01 0.001520058 4 3
## 45 2.250819e-12 1.549313e-03 1.584193e-06 0.999951166 5 5
## 46 1.473672e-11 3.948985e-04 1.051346e-02 0.999258026 5 4
## 47 6.899357e-11 1.062270e-02 1.088930e-02 0.984671096 5 5
## 48 4.656211e-13 2.290727e-08 1.341556e-02 0.999960230 5 3
## 49 2.305951e-16 2.547360e-08 3.276179e-06 0.999999845 5 5
## 50 3.297358e-13 4.878389e-04 3.542370e-02 0.998604250 5 5
## 51 9.253791e-12 2.477073e-05 7.400131e-03 0.999858401 5 4
## 52 1.237675e-12 2.262654e-06 7.853942e-02 0.999760558 5 5
## 53 3.377206e-11 8.759486e-05 7.814669e-01 0.981250553 5 4
## 54 3.232568e-10 5.331411e-04 4.932273e-01 0.980867177 5 4
## 55 1.462543e-10 3.317867e-03 2.379387e-03 0.999677003 5 4
## 56 1.867125e-10 3.181334e-05 1.459739e-02 0.997593732 5 5
## 57 6.127693e-11 1.621743e-04 4.352489e-02 0.998894750 5 2
## 58 1.645445e-15 2.342122e-08 1.700546e-01 0.999705102 5 5
## 59 1.368008e-11 1.455042e-05 1.143775e-02 0.999528342 5 4
## 60 5.694557e-11 5.185868e-04 6.098719e-01 0.954357091 5 4
## 61 2.094117e-12 2.380453e-03 6.248655e-04 0.999784914 5 5
## 62 6.042752e-13 4.741370e-03 8.718178e-04 0.999675989 5 5
## 63 4.864104e-13 9.720062e-06 7.472039e-02 0.999323518 5 4
## 64 7.206423e-15 7.159892e-05 1.071134e-01 0.994396561 5 5
## 65 9.352260e-17 2.927800e-08 4.564051e-07 0.999999993 5 5
## 66 1.375132e-10 1.044508e-07 7.349129e-05 0.999995627 5 5
## 67 1.326586e-12 3.662118e-04 4.317271e-03 0.999818224 5 3
## 68 5.106944e-11 1.682085e-03 8.979678e-02 0.987772219 5 3
## 69 2.629551e-15 1.073282e-05 8.750716e-01 0.974080250 5 4
## 70 1.208068e-11 1.855264e-03 2.998699e-02 0.997161591 5 4
## 71 3.097973e-12 9.781653e-07 2.929753e-02 0.999470394 5 5
## 72 7.682498e-14 3.491678e-09 8.064251e-04 0.999998050 5 5
## 73 7.213120e-09 2.566845e-04 2.730933e-01 0.968476633 5 2
## 74 6.530119e-09 2.757013e-04 2.549625e-01 0.970401074 5 4
## 75 7.637253e-09 1.716341e-05 1.187086e-01 0.758309432 5 4
## 76 2.257566e-13 1.156959e-02 9.639752e-01 0.068207944 4 5
## 77 9.235732e-14 1.696645e-06 9.999990e-01 0.002289955 4 3
## 78 1.095623e-11 2.195774e-07 3.684053e-01 0.996225749 5 4
## 79 2.166294e-09 2.169098e-03 1.096392e-01 0.991571514 5 5
## 80 2.905644e-18 8.443860e-08 1.115403e-01 0.999704535 5 5
## 81 1.544934e-14 3.550888e-07 2.885393e-03 0.999995493 5 5
## 82 1.581843e-14 4.293367e-08 2.872968e-06 0.999999938 5 4
## 83 9.998324e-01 1.663443e-04 1.238672e-03 0.027239616 2 5
## 84 1.594487e-17 1.796796e-08 1.201575e-04 0.999999862 5 5
## 85 1.422776e-14 1.132826e-05 2.659321e-05 0.999999002 5 5
## 86 4.468122e-02 1.452645e-01 7.931956e-04 0.021855840 3 3
## 87 3.464035e-13 3.748193e-03 1.564791e-06 0.999993155 5 4
## 88 7.846061e-10 3.727272e-04 9.985471e-01 0.156288652 4 5
## 89 1.660016e-16 4.345004e-08 9.999969e-01 0.040151826 4 5
## 90 6.715316e-13 1.417874e-06 3.143174e-04 0.999997718 5 3
## 91 2.768488e-12 1.476594e-02 1.150267e-02 0.965510627 5 3
## 92 6.056657e-14 3.022654e-04 9.996661e-01 0.003126025 4 5
## 93 4.750743e-12 1.389049e-05 9.970529e-01 0.448328579 4 4
## 94 6.817468e-11 3.846472e-05 9.957247e-01 0.272606560 4 5
## 95 2.120974e-12 9.561868e-04 1.959761e-02 0.998667535 5 5
## 96 2.858004e-12 3.015733e-05 9.997490e-01 0.043614595 4 5
## 97 3.242211e-12 6.663827e-05 2.761228e-02 0.999632884 5 5
## 98 2.573407e-16 1.041484e-01 4.562950e-01 0.662872115 5 5
## 99 6.918874e-08 2.321362e-05 5.101505e-05 0.999752507 5 2
## 100 3.902271e-12 3.023204e-03 1.994198e-01 0.980194859 5 5CM <- table(Evaluation$Actual,Evaluation$Vote)
CM##
## 2 3 4 5
## 2 0 0 1 6
## 3 0 3 3 19
## 4 0 2 13 47
## 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] 0.5625Precision## 2
## 0.5625Recall## 2
## 0.5257116