setwd("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TP/Full")#install.packages("naivebayes")
library(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)
library(caret)## Loading required package: lattice#Import Labels
Labels <- read_excel("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/1.Labels/Source Data.xlsx")
Label <- Labels$Score#Import Features
Features <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TP/Full/Feature Set 1 Full TP.csv")
Features <- Features[-1]#Class 2
Label2 <- list()
for(i in 1:1000){
if(Label[i]==3| Label[i]==4){
Label2[i] <- 1
}else{
Label2[i] <- 0
}
}#As Factor
Label2 <- as.factor(unlist(Label2))#Class 3
Label3 <- list()
for(i in 1:1000){
if(Label[i]==5| Label[i]==6){
Label3[i] <- 1
}else{
Label3[i] <- 0
}
}#As Factor
Label3 <- as.factor(unlist(Label3))#Class 4
Label4 <- list()
for(i in 1:1000){
if(Label[i]==7| Label[i]==8){
Label4[i] <- 1
}else{
Label4[i] <- 0
}
}#As Factor
Label4 <- as.factor(unlist(Label4))#Class 5
Label5 <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
Label5[i] <- 1
}else{
Label5[i] <- 0
}
}#As Factor
Label5 <- as.factor(unlist(Label5))#All Labels
All <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
All[i] <- 5
}else if(Label[i]==7| Label[i]==8){
All[i] <- 4
}else if(Label[i]==5| Label[i]==6){
All[i] <- 3
}else{
All[i] <- 2
}
}#As Factor
All <- as.factor(unlist(All))#Control
Control.df <- data.frame(matrix(seq(1,1000),ncol=1,nrow=1000))
Control.df$Actual <- as.factor(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 1:2672){
Features[,i] <- as.factor(Features[,i])
}
str(Features)## 'data.frame': 1000 obs. of 2672 variables:
## $ abil : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ abit : Factor w/ 2 levels "0","1": 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 ...
## $ abnorm : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ about : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ abov : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ abrupt : Factor w/ 2 levels "0","1": 1 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/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ access : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ accid : Factor w/ 2 levels "0","1": 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 ...
## $ accomplish : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ accur : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ accustom : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ acess : 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 ...
## $ acomod : 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 ...
## $ activ : 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 ...
## $ adaptor : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 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 ...
## $ adjust : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ ador : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ adult : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ advanc : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ advantag : Factor w/ 2 levels "0","1": 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 ...
## $ affair : 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","1": 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 ...
## $ afterdinn : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ afternoon : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ afterward : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ age : 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 ...
## $ agr : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ agreeabl : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ ahead : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ air : Factor w/ 2 levels "0","1": 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 ...
## $ airless : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ airport : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ alarm : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ albeit : Factor w/ 2 levels "0","1": 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 ...
## $ albrt : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ alcohol : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ aldo : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ alittl : Factor w/ 2 levels "0","1": 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 ...
## $ allevi : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ alloc : Factor w/ 2 levels "0","1": 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 ...
## $ alongsid : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ alot : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ alreadi : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ...
## $ alright : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ also : Factor w/ 2 levels "0","1": 1 1 1 2 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/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ alway : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ amaz : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
## $ ambianc : Factor w/ 2 levels "0","1": 1 1 1 1 1 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 ...
## $ amend : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ america : Factor w/ 2 levels "0","1": 1 1 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 ...
## $ amongst : 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 ...
## $ ampl : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ amsterdam : Factor w/ 2 levels "0","1": 1 2 1 2 1 1 1 1 1 1 ...
## $ and : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ angl : Factor w/ 2 levels "0","1": 1 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 ...
## $ ann : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ anna : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ annex : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ announc : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ annoy : Factor w/ 2 levels "0","1": 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 ...
## $ ansterdam : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ answer : 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
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## 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 1.000000 5.085547e-17 0.6298040 3.701960e-01 0.000286024 9.997140e-01
## 2 1.000000 5.132245e-17 1.0000000 4.599657e-08 0.999991388 8.612184e-06
## 3 1.000000 3.700019e-19 0.9999990 9.936373e-07 0.998186130 1.813870e-03
## 4 1.000000 2.147626e-12 1.0000000 3.340118e-08 0.999786944 2.130561e-04
## 5 0.999998 2.038750e-06 0.9572649 4.273511e-02 0.924065820 7.593418e-02
## 6 1.000000 2.207549e-14 0.8393824 1.606176e-01 0.990092212 9.907788e-03
## Class 5: 0 Class5: 1
## 1 1.000000e+00 3.590963e-13
## 2 3.135763e-05 9.999686e-01
## 3 1.690535e-03 9.983095e-01
## 4 9.879116e-01 1.208836e-02
## 5 9.771605e-01 2.283952e-02
## 6 9.790841e-01 2.091591e-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 5.085547e-17 3.701960e-01 9.997140e-01 3.590963e-13
## 2 5.132245e-17 4.599657e-08 8.612184e-06 9.999686e-01
## 3 3.700019e-19 9.936373e-07 1.813870e-03 9.983095e-01
## 4 2.147626e-12 3.340118e-08 2.130561e-04 1.208836e-02
## 5 2.038750e-06 4.273511e-02 7.593418e-02 2.283952e-02
## 6 2.207549e-14 1.606176e-01 9.907788e-03 2.091591e-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 5.085547e-17 3.701960e-01 9.997140e-01 3.590963e-13 4 4
## 2 5.132245e-17 4.599657e-08 8.612184e-06 9.999686e-01 5 5
## 3 3.700019e-19 9.936373e-07 1.813870e-03 9.983095e-01 5 5
## 4 2.147626e-12 3.340118e-08 2.130561e-04 1.208836e-02 5 4
## 5 2.038750e-06 4.273511e-02 7.593418e-02 2.283952e-02 4 4
## 6 2.207549e-14 1.606176e-01 9.907788e-03 2.091591e-02 3 4
## 7 4.482175e-13 2.370420e-12 6.236825e-05 1.245384e-07 4 5
## 8 7.010302e-15 2.305498e-01 9.995794e-01 1.821500e-12 4 3
## 9 2.666696e-18 6.226118e-10 1.220446e-01 9.574927e-01 5 5
## 10 8.590340e-11 8.176714e-01 5.506424e-06 1.829595e-06 3 3
## 11 3.251670e-20 1.609038e-06 7.436063e-01 4.022727e-01 4 4
## 12 1.654985e-09 4.789040e-04 8.064601e-01 1.324861e-02 4 3
## 13 4.284540e-16 4.708144e-04 2.058908e-03 9.915386e-01 5 5
## 14 1.362932e-07 1.593681e-01 5.024963e-01 1.348096e-06 4 4
## 15 7.337653e-09 9.614237e-01 8.627804e-02 7.833711e-04 3 4
## 16 4.676808e-22 2.868356e-09 1.408753e-01 1.768022e-01 5 5
## 17 5.303401e-16 1.852825e-03 4.265215e-03 1.858882e-01 5 4
## 18 3.852268e-13 1.808042e-07 8.917750e-02 9.934811e-01 5 5
## 19 3.267149e-11 1.970857e-01 6.554830e-01 1.114613e-03 4 4
## 20 4.136127e-12 2.650023e-04 2.222324e-01 7.808297e-01 5 2
## 21 6.024745e-18 5.846514e-04 9.814748e-01 1.836108e-07 4 4
## 22 2.320499e-26 6.617775e-05 6.039513e-01 6.540577e-01 5 4
## 23 3.814672e-05 9.997395e-01 9.591423e-01 5.332633e-14 3 3
## 24 3.789730e-21 9.933866e-07 7.629092e-02 9.997544e-01 5 5
## 25 5.086220e-13 1.211110e-01 2.123943e-01 6.831716e-02 4 4
## 26 1.579914e-13 3.460893e-03 1.345149e-01 7.337430e-01 5 5
## 27 5.664919e-14 3.303206e-08 7.974759e-01 8.140434e-01 5 4
## 28 3.674182e-11 2.664631e-05 4.726364e-03 9.389100e-01 5 5
## 29 1.387636e-01 5.359010e-03 4.580873e-02 3.974114e-09 2 3
## 30 6.277883e-08 3.647405e-01 9.671337e-02 4.384247e-16 3 3
## 31 1.094219e-15 1.561389e-06 1.153268e-03 9.998369e-01 5 5
## 32 1.541516e-15 1.542555e-04 4.474201e-05 9.994096e-01 5 5
## 33 6.268604e-19 2.390352e-05 9.747067e-01 8.376245e-01 4 5
## 34 9.790813e-15 9.882669e-01 3.106137e-01 8.173468e-06 3 3
## 35 1.235895e-13 8.076226e-01 1.816510e-01 4.928246e-03 3 4
## 36 9.031086e-17 7.988430e-01 8.089730e-01 1.021642e-02 4 4
## 37 1.750806e-09 5.202050e-06 9.997740e-01 4.396381e-10 4 5
## 38 9.992761e-01 9.970891e-01 4.176811e-01 1.274321e-09 2 3
## 39 5.150557e-19 6.957228e-07 2.171225e-04 9.997894e-01 5 5
## 40 3.958031e-12 4.853673e-03 9.995448e-01 6.062867e-09 4 3
## 41 8.404244e-17 6.694519e-07 4.159107e-04 9.999791e-01 5 5
## 42 2.692873e-19 8.047690e-01 8.441047e-01 2.180265e-04 4 4
## 43 3.892315e-13 1.792629e-14 6.688933e-01 4.107468e-05 4 5
## 44 2.106748e-12 9.247941e-04 9.164247e-01 1.379230e-02 4 3
## 45 1.670353e-12 1.036277e-02 9.772343e-01 4.470117e-02 4 5
## 46 1.305538e-08 3.928772e-03 8.622768e-01 3.508936e-02 4 4
## 47 9.798676e-14 2.207403e-05 8.905776e-01 8.112612e-01 4 5
## 48 2.039372e-12 2.551856e-01 9.606574e-02 3.601320e-01 5 3
## 49 1.549235e-15 1.339567e-06 6.812237e-03 9.999814e-01 5 5
## 50 2.383341e-10 1.242242e-05 4.351616e-03 9.861694e-01 5 5
## 51 2.635576e-16 8.217595e-06 8.852001e-03 9.999521e-01 5 4
## 52 1.117418e-11 2.944834e-06 9.903020e-01 3.529115e-02 4 5
## 53 6.313133e-15 7.282005e-03 4.413418e-03 9.901109e-01 5 4
## 54 8.894070e-16 1.278438e-06 9.499966e-01 7.447794e-01 4 4
## 55 5.177564e-14 1.076860e-02 1.406200e-01 8.916769e-01 5 4
## 56 1.865504e-15 1.589108e-06 4.092820e-04 9.999943e-01 5 5
## 57 5.122882e-14 1.302572e-05 1.891993e-02 9.998712e-01 5 2
## 58 2.164496e-16 1.092532e-07 1.171357e-02 9.999674e-01 5 5
## 59 6.207655e-17 2.012663e-06 3.730958e-03 9.999892e-01 5 4
## 60 3.703190e-13 2.437926e-01 9.307587e-01 1.639783e-02 4 4
## 61 1.415214e-13 1.901461e-05 1.681163e-02 9.998169e-01 5 5
## 62 1.293811e-14 1.767515e-04 6.445276e-04 9.997690e-01 5 5
## 63 9.310234e-11 6.329144e-03 6.298900e-01 7.757074e-01 5 4
## 64 2.896868e-17 1.968502e-06 3.382364e-01 9.984799e-01 5 5
## 65 6.053089e-18 2.891696e-07 2.210123e-03 9.999923e-01 5 5
## 66 3.309020e-16 1.884755e-09 1.635538e-03 9.999985e-01 5 5
## 67 6.675858e-11 8.610071e-06 7.294747e-02 9.806083e-01 5 3
## 68 8.217342e-12 1.167028e-02 1.310860e-01 9.411885e-01 5 3
## 69 3.026150e-15 3.020766e-04 8.940019e-02 9.791349e-01 5 4
## 70 8.983756e-20 1.430739e-07 1.213226e-01 9.983816e-01 5 4
## 71 4.253927e-15 3.342130e-07 6.278586e-03 9.999808e-01 5 5
## 72 5.791291e-17 1.732431e-07 3.073322e-01 9.983583e-01 5 5
## 73 1.479631e-10 1.816138e-03 1.820248e-01 9.101305e-01 5 2
## 74 3.166153e-10 1.507340e-03 1.128675e-01 9.375731e-01 5 4
## 75 1.904695e-15 1.904132e-06 4.503018e-01 9.964554e-01 5 4
## 76 4.843697e-17 1.278937e-06 4.524668e-01 8.225276e-01 5 5
## 77 2.294928e-17 1.820332e-07 1.430221e-02 1.518888e-02 5 3
## 78 1.115960e-17 4.824486e-05 4.280223e-01 5.877863e-01 5 4
## 79 1.903349e-21 9.560638e-01 8.773338e-01 1.574645e-09 3 5
## 80 3.855061e-16 1.048221e-09 1.132342e-03 9.999966e-01 5 5
## 81 8.883978e-20 1.589786e-07 1.134751e-01 9.955866e-01 5 5
## 82 6.441821e-20 3.352122e-06 5.545280e-02 9.997791e-01 5 4
## 83 5.528756e-21 2.233603e-10 1.848684e-01 6.007386e-07 4 5
## 84 1.937846e-14 4.552182e-06 1.285113e-03 9.987333e-01 5 5
## 85 1.315913e-16 4.164742e-04 1.748855e-01 8.887877e-01 5 5
## 86 1.069401e-17 2.003729e-05 6.124875e-03 9.186108e-05 4 3
## 87 2.600914e-03 8.057695e-04 9.540313e-01 3.636497e-09 4 4
## 88 2.777176e-15 6.089107e-05 3.118155e-03 5.353485e-02 5 5
## 89 1.596456e-23 2.389506e-07 1.993851e-02 9.999079e-01 5 5
## 90 7.658002e-15 6.973426e-06 7.934139e-01 1.453197e-02 4 3
## 91 1.299444e-16 1.912535e-01 7.684274e-02 9.110384e-01 5 3
## 92 2.630457e-17 1.541552e-02 2.788443e-01 2.168203e-01 4 5
## 93 6.329448e-21 7.566772e-04 9.955079e-01 6.995435e-04 4 4
## 94 2.410715e-17 2.322570e-12 1.189808e-02 9.204965e-05 4 5
## 95 1.195888e-12 1.073497e-04 3.422524e-04 9.867647e-01 5 5
## 96 3.491141e-25 2.754688e-07 3.699775e-01 9.978946e-01 5 5
## 97 1.046920e-20 4.734983e-05 5.089225e-02 9.952704e-01 5 5
## 98 3.780183e-14 8.511085e-06 3.782028e-03 9.809567e-01 5 5
## 99 6.130152e-05 3.122695e-03 1.535329e-03 1.779499e-01 5 2
## 100 4.924314e-15 1.405987e-02 5.583749e-01 4.365679e-01 4 5CM <- table(Evaluation$Actual,Evaluation$Vote)
CM##
## 2 3 4 5
## 2 0 0 1 6
## 3 2 6 8 9
## 4 0 5 24 33
## 5 0 2 17 95#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] 0.6009615Precision## 2
## 0.6009615Recall## 2
## 0.5626573