setwd("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TP/90")#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)#Import Labels
Labels <- read_excel("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/1.Labels/Source Data.xlsx")
Label <- Labels$Score#Import Features
Features <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TP/90/Feature Set 1 90th TP.csv")
Features <- Features[-1]#Class 2
Label2 <- list()
for(i in 1:1000){
if(Label[i]==3| Label[i]==4){
Label2[i] <- 1
}else{
Label2[i] <- 0
}
}#As Factor
Label2 <- as.factor(unlist(Label2))#Class 3
Label3 <- list()
for(i in 1:1000){
if(Label[i]==5| Label[i]==6){
Label3[i] <- 1
}else{
Label3[i] <- 0
}
}#As Factor
Label3 <- as.factor(unlist(Label3))#Class 4
Label4 <- list()
for(i in 1:1000){
if(Label[i]==7| Label[i]==8){
Label4[i] <- 1
}else{
Label4[i] <- 0
}
}#As Factor
Label4 <- as.factor(unlist(Label4))#Class 5
Label5 <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
Label5[i] <- 1
}else{
Label5[i] <- 0
}
}#As Factor
Label5 <- as.factor(unlist(Label5))#All Labels
All <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
All[i] <- 5
}else if(Label[i]==7| Label[i]==8){
All[i] <- 4
}else if(Label[i]==5| Label[i]==6){
All[i] <- 3
}else{
All[i] <- 2
}
}#As Factor
All <- as.factor(unlist(All))#Control
Control.df <- data.frame(matrix(seq(1,1000),ncol=1,nrow=1000))
Control.df$Actual <- 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 1:262){
Features[,i] <- as.factor(Features[,i])
}
str(Features)## 'data.frame': 1000 obs. of 262 variables:
## $ access : 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 ...
## $ airport : 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 ...
## $ 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 ...
## $ 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 ...
## $ area : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ...
## $ around : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ...
## $ arriv : Factor w/ 2 levels "0","1": 2 1 1 1 2 1 1 1 1 1 ...
## $ ask : Factor w/ 2 levels "0","1": 2 1 2 1 1 1 1 1 1 1 ...
## $ avail : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ away : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ back : Factor w/ 2 levels "0","1": 1 2 2 1 1 1 1 1 1 1 ...
## $ bad : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ...
## $ bar : Factor w/ 2 levels "0","1": 1 1 2 2 1 1 1 1 1 1 ...
## $ bath : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ bathroom : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ beauti : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 2 1 1 1 ...
## $ bed : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 2 1 1 2 ...
## $ bedroom : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ best : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ better : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ big : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 2 ...
## $ bit : Factor w/ 2 levels "0","1": 1 2 2 1 1 1 1 1 1 1 ...
## $ book : Factor w/ 2 levels "0","1": 2 1 1 1 2 1 1 1 1 1 ...
## $ breakfast : Factor w/ 2 levels "0","1": 1 1 2 1 1 1 1 2 1 2 ...
## $ buffet : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ build : Factor w/ 2 levels "0","1": 1 1 1 2 2 1 1 1 1 1 ...
## $ busi : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ but : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ can : Factor w/ 2 levels "0","1": 2 2 1 1 1 1 1 1 1 1 ...
## $ center : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ centr : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ central : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ chang : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 2 1 1 1 ...
## $ charg : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ check : Factor w/ 2 levels "0","1": 2 2 1 1 1 1 1 1 2 1 ...
## $ choic : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ citi : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ clean : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 2 1 2 1 ...
## $ close : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ coff : Factor w/ 2 levels "0","1": 1 1 2 1 1 1 1 1 1 1 ...
## $ cold : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ come : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ comfi : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ comfort : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ complet : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
## $ condit : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ construct : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ conveni : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ cool : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ couldn : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ court : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ day : Factor w/ 2 levels "0","1": 2 1 1 2 1 1 1 1 1 1 ...
## $ decor : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ definit : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ design : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...
## $ desk : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ didn : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 2 1 1 1 ...
## $ differ : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ...
## $ direct : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
## $ don : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ door : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ...
## $ doubl : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ drink : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ due : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ earl : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ easi : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ english : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ enjoy : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 1 ...
## $ enough : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 2 ...
## $ especi : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
## $ etc : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ even : Factor w/ 2 levels "0","1": 2 2 1 1 1 1 1 1 2 2 ...
## $ everi : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ everyth : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 1 ...
## $ excel : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
## $ except : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ expect : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ expen : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ experi : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ extra : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ extrem : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ facil : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ fantast : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
## $ far : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ feel : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ find : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ first : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
## $ floor : Factor w/ 2 levels "0","1": 2 1 1 2 1 1 2 1 1 1 ...
## $ food : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 2 1 2 ...
## $ free : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ fresh : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ friend : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 1 ...
## $ front : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ garden : Factor w/ 2 levels "0","1": 2 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
## 8 4 0 0 1
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## 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 0.9999964 3.577262e-06 0.6773362 0.3226637802 0.2881494 0.71185061
## 2 1.0000000 1.296215e-08 0.9996737 0.0003263259 0.5201778 0.47982223
## 3 1.0000000 3.923847e-09 0.9995553 0.0004447481 0.9750446 0.02495536
## 4 1.0000000 3.996712e-11 0.9837265 0.0162734746 0.8207217 0.17927831
## 5 0.6043338 3.956662e-01 0.7403262 0.2596737966 0.6341328 0.36586722
## 6 0.9999915 8.460940e-06 0.8704054 0.1295945629 0.5874258 0.41257422
## Class 5: 0 Class5: 1
## 1 0.9999394 0.0000606422
## 2 0.4665257 0.5334742746
## 3 0.1311168 0.8688831560
## 4 0.9668767 0.0331232900
## 5 0.9920634 0.0079366492
## 6 0.8641142 0.1358857568Transformed.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 3.577262e-06 0.3226637802 0.71185061 0.0000606422
## 2 1.296215e-08 0.0003263259 0.47982223 0.5334742746
## 3 3.923847e-09 0.0004447481 0.02495536 0.8688831560
## 4 3.996712e-11 0.0162734746 0.17927831 0.0331232900
## 5 3.956662e-01 0.2596737966 0.36586722 0.0079366492
## 6 8.460940e-06 0.1295945629 0.41257422 0.1358857568Evaluation <- 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 3.577262e-06 3.226638e-01 0.7118506120 6.064220e-05 4 4
## 2 1.296215e-08 3.263259e-04 0.4798222287 5.334743e-01 5 5
## 3 3.923847e-09 4.447481e-04 0.0249553612 8.688832e-01 5 5
## 4 3.996712e-11 1.627347e-02 0.1792783093 3.312329e-02 4 4
## 5 3.956662e-01 2.596738e-01 0.3658672237 7.936649e-03 2 4
## 6 8.460940e-06 1.295946e-01 0.4125742161 1.358858e-01 4 4
## 7 3.648811e-06 1.114918e-03 0.0018768257 5.525458e-04 4 5
## 8 1.402814e-05 3.177300e-02 0.9964997601 3.333876e-06 4 3
## 9 2.183181e-10 3.817915e-08 0.0159907647 9.988016e-01 5 5
## 10 3.085447e-06 2.325662e-01 0.0016431279 1.098981e-02 3 3
## 11 4.150167e-12 4.258311e-03 0.3792330875 6.763455e-01 5 4
## 12 2.553199e-04 5.473812e-02 0.2507647518 3.301701e-01 5 3
## 13 8.270672e-04 1.296225e-02 0.0953042583 6.572528e-01 5 5
## 14 5.260821e-02 6.148077e-02 0.2498397712 1.652991e-03 4 4
## 15 1.922885e-01 4.724900e-02 0.4560214922 3.221626e-02 4 4
## 16 1.508914e-04 8.962236e-02 0.0393596967 2.820803e-01 5 5
## 17 7.927731e-08 3.833862e-05 0.0254192747 6.149799e-01 5 4
## 18 3.958987e-05 7.399512e-03 0.4089204580 6.597893e-01 5 5
## 19 1.465964e-03 5.335669e-01 0.5470968703 5.110704e-03 4 4
## 20 1.907777e-07 1.144642e-01 0.6330170217 1.397914e-01 4 2
## 21 3.780651e-08 2.463318e-01 0.9486959613 2.283441e-05 4 4
## 22 6.944054e-13 2.041519e-02 0.1297679925 9.309713e-01 5 4
## 23 5.592151e-04 9.845960e-01 0.4283692219 1.761364e-05 3 3
## 24 4.800817e-11 4.129987e-05 0.3773931856 9.494590e-01 5 5
## 25 1.392328e-06 3.090581e-01 0.7800501541 4.470319e-02 4 4
## 26 2.015931e-05 7.701377e-02 0.0378596669 6.621002e-01 5 5
## 27 2.345171e-06 4.447719e-04 0.5424902114 2.600469e-01 4 4
## 28 1.316869e-03 6.119563e-02 0.0302791495 4.614604e-01 5 5
## 29 1.269678e-03 1.640509e-01 0.3132860121 3.368292e-03 4 3
## 30 9.971787e-01 6.288412e-01 0.0608290053 1.637617e-06 2 3
## 31 1.951849e-07 1.849324e-04 0.0060749732 9.941944e-01 5 5
## 32 3.829961e-09 1.589082e-03 0.0009648995 9.900930e-01 5 5
## 33 5.348479e-11 4.302691e-03 0.7880489692 5.324961e-01 4 5
## 34 2.982265e-07 9.916278e-01 0.7464702802 2.928715e-04 3 3
## 35 2.412968e-06 1.091282e-01 0.7254977168 9.944669e-02 4 4
## 36 2.036242e-06 5.459409e-01 0.5760036142 6.916925e-02 4 4
## 37 2.183730e-01 6.446437e-01 0.9735060905 1.374876e-05 4 5
## 38 9.995858e-01 9.998519e-01 0.4869545521 1.126256e-05 3 3
## 39 1.771777e-14 3.967329e-06 0.0026730342 9.991871e-01 5 5
## 40 5.480362e-03 7.911005e-01 0.9672022064 9.342631e-06 4 3
## 41 2.380225e-09 9.670902e-06 0.0012593989 9.993828e-01 5 5
## 42 1.822218e-08 9.940063e-01 0.7662050639 5.660461e-05 3 4
## 43 8.281100e-08 8.813637e-09 0.5015959120 1.510495e-03 4 5
## 44 6.454543e-05 1.173544e-01 0.7507670872 6.349329e-02 4 3
## 45 2.815548e-05 6.422459e-01 0.9378705836 4.135271e-03 4 5
## 46 6.915940e-02 1.398510e-01 0.6338413481 1.386416e-02 4 4
## 47 2.338130e-04 1.200494e-01 0.3562172683 4.140196e-01 5 5
## 48 1.331776e-05 1.654751e-01 0.5077433330 2.400168e-01 4 3
## 49 4.457457e-08 4.342755e-05 0.0194935502 9.983768e-01 5 5
## 50 3.963646e-07 1.347061e-03 0.1370162213 9.160735e-01 5 5
## 51 7.583079e-09 2.663494e-04 0.0252339613 9.958378e-01 5 4
## 52 5.198301e-05 3.557606e-03 0.7802881775 1.297637e-01 4 5
## 53 1.620318e-08 8.037782e-02 0.0209530860 8.841686e-01 5 4
## 54 5.144474e-09 1.060199e-03 0.5543361964 7.488428e-01 5 4
## 55 1.177605e-05 7.518233e-01 0.0959435984 1.847825e-01 3 4
## 56 1.014648e-07 4.120210e-04 0.0107693404 9.951377e-01 5 5
## 57 1.473953e-06 4.221269e-04 0.0529389257 9.888778e-01 5 2
## 58 1.160954e-07 7.175829e-05 0.0150942782 9.976317e-01 5 5
## 59 2.158902e-08 9.286615e-04 0.0518107182 9.889387e-01 5 4
## 60 1.938025e-03 8.418535e-01 0.8999690082 8.210440e-04 4 4
## 61 4.071836e-06 6.160945e-04 0.0472222603 9.842695e-01 5 5
## 62 1.991389e-07 2.993151e-03 0.0962497004 9.470794e-01 5 5
## 63 1.328173e-03 9.600748e-02 0.8509809888 5.851528e-02 4 4
## 64 1.437344e-08 8.909067e-05 0.1671483839 9.774306e-01 5 5
## 65 1.741594e-10 9.374926e-06 0.0063794041 9.993249e-01 5 5
## 66 1.006152e-07 7.013440e-07 0.0149710913 9.992227e-01 5 5
## 67 3.030062e-05 2.062283e-03 0.0849101405 8.346600e-01 5 3
## 68 3.167626e-05 1.665638e-01 0.2661544739 4.690451e-01 5 3
## 69 1.800220e-07 6.762006e-02 0.5647347340 2.599804e-01 4 4
## 70 4.531948e-10 4.580273e-04 0.7165152366 4.757308e-01 4 4
## 71 4.818658e-07 4.630274e-05 0.0163228925 9.971977e-01 5 5
## 72 1.206706e-07 3.350888e-05 0.1058603991 9.728516e-01 5 5
## 73 2.240126e-04 4.841141e-02 0.3269414998 4.966250e-01 5 2
## 74 6.204563e-04 1.729766e-01 0.2504531245 2.918904e-01 5 4
## 75 5.480188e-08 6.172913e-05 0.7036546248 7.630840e-01 5 4
## 76 3.318144e-12 7.298551e-04 0.6808534676 6.189777e-01 4 5
## 77 1.212893e-11 5.073257e-05 0.0633758024 4.710496e-01 5 3
## 78 1.075800e-09 5.166955e-04 0.5777524192 6.377276e-01 5 4
## 79 5.818139e-12 1.776482e-01 0.4085401497 9.521722e-03 4 5
## 80 1.726586e-07 2.431073e-06 0.0097050496 9.979346e-01 5 5
## 81 1.492814e-12 5.101925e-05 0.3636630419 8.320933e-01 5 5
## 82 1.853441e-12 1.086659e-04 0.1454230137 9.810841e-01 5 4
## 83 1.959869e-14 1.674642e-05 0.6765803241 2.008529e-03 4 5
## 84 7.247222e-09 3.555994e-04 0.0010586424 9.984394e-01 5 5
## 85 1.067889e-07 1.477541e-02 0.3178838915 1.710479e-01 4 5
## 86 7.218130e-10 8.628125e-01 0.8711646098 8.926872e-04 4 3
## 87 1.100728e-03 4.643986e-01 0.2748789016 4.274822e-02 3 4
## 88 2.612415e-05 4.074979e-02 0.1620142536 1.230450e-02 4 5
## 89 4.129829e-16 1.748959e-06 0.0118360617 9.996305e-01 5 5
## 90 2.598205e-08 1.843607e-04 0.2373249714 6.572742e-01 5 3
## 91 1.299511e-07 7.376286e-02 0.2341766631 5.474587e-01 5 3
## 92 4.537031e-09 2.343571e-01 0.6316256541 2.527882e-02 4 5
## 93 8.240172e-09 8.544888e-01 0.8943007926 7.143626e-04 4 4
## 94 1.225032e-13 2.081479e-06 0.3163355124 1.284710e-01 4 5
## 95 1.013216e-06 5.666493e-04 0.2794964551 5.727221e-01 5 5
## 96 2.150237e-17 9.615053e-06 0.0620929425 9.963173e-01 5 5
## 97 3.029427e-10 7.807015e-05 0.0894834485 9.673206e-01 5 5
## 98 1.641798e-03 4.310680e-01 0.0091352064 3.816393e-01 3 5
## 99 5.745565e-02 2.767428e-03 0.5224682875 1.530876e-01 4 2
## 100 2.035117e-07 4.638831e-02 0.5827610852 2.300096e-01 4 5CM <- table(Evaluation$Actual,Evaluation$Vote)
CM##
## 2 3 4 5
## 2 0 0 3 4
## 3 1 6 9 9
## 4 1 6 28 27
## 5 0 2 21 91#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
## 0.5690584