setwd("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TF/90")#install.packages("naivebayes")
#install.packages("Metrics")
library(Metrics)## Warning: package 'Metrics' was built under R version 3.4.4library(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$Score#Import Features
Features <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TF/90/Feature Set 1 90th Percentile.csv")
Features <- Features[-1]#Class 2
Label2 <- list()
for(i in 1:1000){
if(Label[i]==3| Label[i]==4){
Label2[i] <- 1
}else{
Label2[i] <- 0
}
}#As Factor
Label2 <- as.factor(unlist(Label2))#Class 3
Label3 <- list()
for(i in 1:1000){
if(Label[i]==5| Label[i]==6){
Label3[i] <- 1
}else{
Label3[i] <- 0
}
}#As Factor
Label3 <- as.factor(unlist(Label3))#Class 4
Label4 <- list()
for(i in 1:1000){
if(Label[i]==7| Label[i]==8){
Label4[i] <- 1
}else{
Label4[i] <- 0
}
}#As Factor
Label4 <- as.factor(unlist(Label4))#Class 5
Label5 <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
Label5[i] <- 1
}else{
Label5[i] <- 0
}
}#As Factor
Label5 <- as.factor(unlist(Label5))#All Labels
All <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
All[i] <- 5
}else if(Label[i]==7| Label[i]==8){
All[i] <- 4
}else if(Label[i]==5| Label[i]==6){
All[i] <- 3
}else{
All[i] <- 2
}
}#As Factor
All <- as.factor(unlist(All))#Control
Control.df <- data.frame(matrix(seq(1,1000),ncol=1,nrow=1000))
Control.df$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 1:264){
Features[,i] <- as.factor(Features[,i])
}
str(Features)## 'data.frame': 1000 obs. of 264 variables:
## $ access : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ air : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ airport : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ also : Factor w/ 5 levels "0","1","2","3",..: 1 1 1 4 1 1 2 1 2 1 ...
## $ although : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ alway : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ amaz : Factor w/ 3 levels "0","1","2": 1 1 1 1 2 1 1 1 1 1 ...
## $ amsterdam : Factor w/ 3 levels "0","1","2": 1 2 1 2 1 1 1 1 1 1 ...
## $ and : Factor w/ 3 levels "0","1","2": 2 1 1 1 1 1 1 1 1 1 ...
## $ area : Factor w/ 3 levels "0","1","2": 1 1 1 2 1 1 1 1 1 1 ...
## $ around : Factor w/ 3 levels "0","1","2": 1 1 1 2 1 1 1 1 1 1 ...
## $ arriv : Factor w/ 4 levels "0","1","2","3": 2 1 1 1 2 1 1 1 1 1 ...
## $ ask : Factor w/ 3 levels "0","1","2": 2 1 2 1 1 1 1 1 1 1 ...
## $ avail : Factor w/ 3 levels "0","1","2": 2 1 1 1 1 1 1 1 1 1 ...
## $ away : Factor w/ 4 levels "0","1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ back : Factor w/ 4 levels "0","1","2","3": 1 2 2 1 1 1 1 1 1 1 ...
## $ bad : Factor w/ 3 levels "0","1","2": 1 1 1 2 1 1 1 1 1 1 ...
## $ bar : Factor w/ 3 levels "0","1","2": 1 1 2 2 1 1 1 1 1 1 ...
## $ bath : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ bathroom : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ beauti : Factor w/ 3 levels "0","1","2": 2 1 1 1 1 1 2 1 1 1 ...
## $ bed : Factor w/ 4 levels "0","1","2","3": 1 1 1 3 1 1 2 1 1 2 ...
## $ bedroom : Factor w/ 3 levels "0","1","2": 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/ 3 levels "0","1","2": 3 1 1 1 1 1 1 1 1 3 ...
## $ bit : Factor w/ 3 levels "0","1","2": 1 3 2 1 1 1 1 1 1 1 ...
## $ book : Factor w/ 6 levels "0","1","2","3",..: 6 1 1 1 4 1 1 1 1 1 ...
## $ breakfast : Factor w/ 6 levels "0","1","2","3",..: 1 1 2 1 1 1 1 2 1 2 ...
## $ buffet : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ build : Factor w/ 4 levels "0","1","2","3": 1 1 1 3 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/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ can : Factor w/ 4 levels "0","1","2","3": 4 2 1 1 1 1 1 1 1 1 ...
## $ center : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ centr : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ central : Factor w/ 4 levels "0","1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ chang : Factor w/ 3 levels "0","1","2": 3 1 1 1 1 1 3 1 1 1 ...
## $ charg : Factor w/ 4 levels "0","1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ check : Factor w/ 4 levels "0","1","2","3": 3 2 1 1 1 1 1 1 2 1 ...
## $ choic : Factor w/ 3 levels "0","1","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ citi : Factor w/ 4 levels "0","1","2","3": 2 1 1 1 1 1 1 1 1 1 ...
## $ clean : Factor w/ 5 levels "0","1","2","3",..: 1 1 1 3 1 1 2 1 2 1 ...
## $ close : Factor w/ 4 levels "0","1","2","3": 2 1 1 1 1 1 1 1 1 1 ...
## $ coff : Factor w/ 4 levels "0","1","2","3": 1 1 2 1 1 1 1 1 1 1 ...
## $ cold : Factor w/ 4 levels "0","1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ come : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ comfi : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ comfort : Factor w/ 4 levels "0","1","2","3": 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/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ construct : Factor w/ 4 levels "0","1","2","3": 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/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ court : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ day : Factor w/ 4 levels "0","1","2","3": 4 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/ 4 levels "0","1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ design : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 2 1 1 1 1 ...
## $ desk : Factor w/ 4 levels "0","1","2","4": 1 1 1 1 1 1 1 1 1 1 ...
## $ didn : Factor w/ 4 levels "0","1","2","3": 1 1 1 1 2 1 2 1 1 1 ...
## $ differ : Factor w/ 3 levels "0","1","2": 1 1 1 2 1 1 1 1 1 1 ...
## $ direct : Factor w/ 3 levels "0","1","2": 1 1 1 1 2 1 1 1 1 1 ...
## $ dirti : Factor w/ 4 levels "0","1","2","3": 1 1 1 4 1 1 1 1 2 1 ...
## $ don : Factor w/ 3 levels "0","1","3": 2 1 1 1 1 1 1 1 1 1 ...
## $ door : Factor w/ 3 levels "0","1","2": 1 1 1 3 1 1 1 1 1 1 ...
## $ doubl : Factor w/ 3 levels "0","1","2": 2 1 1 1 1 1 1 1 1 1 ...
## $ drink : Factor w/ 4 levels "0","1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ due : Factor w/ 3 levels "0","1","2": 2 1 1 1 1 1 1 1 1 1 ...
## $ earl : Factor w/ 3 levels "0","1","2": 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/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ enough : Factor w/ 3 levels "0","1","2": 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/ 4 levels "0","1","2","3": 3 2 1 1 1 1 1 1 2 2 ...
## $ everi : Factor w/ 3 levels "0","1","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ everyth : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 2 1 1 ...
## $ excel : Factor w/ 4 levels "0","1","2","3": 1 2 1 1 1 1 1 1 1 1 ...
## $ expect : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ expen : Factor w/ 4 levels "0","1","2","3": 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/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ extrem : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ facil : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ fantast : Factor w/ 3 levels "0","1","2": 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/ 4 levels "0","1","2","4": 1 1 1 1 1 1 1 1 1 1 ...
## $ find : Factor w/ 3 levels "0","1","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ first : Factor w/ 3 levels "0","1","2": 1 2 1 1 1 1 1 1 1 1 ...
## $ floor : Factor w/ 4 levels "0","1","2","3": 2 1 1 4 1 1 2 1 1 1 ...
## $ food : Factor w/ 3 levels "0","1","2": 1 2 1 1 1 1 1 2 1 2 ...
## $ free : Factor w/ 4 levels "0","1","2","5": 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/ 3 levels "0","1","2": 1 1 1 1 1 1 1 2 1 1 ...
## $ front : Factor w/ 4 levels "0","1","2","4": 1 1 1 1 1 1 1 1 1 1 ...
## $ garden : Factor w/ 3 levels "0","1","2": 2 1 1 1 1 1 1 1 1 1 ...
## $ get : Factor w/ 5 levels "0","1","2","3",..: 2 1 1 1 3 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
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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 0.9998336 1.664222e-04 0.4271259 0.5728740552 0.2421555 0.75784447
## 2 1.0000000 1.290483e-13 0.9998727 0.0001272975 0.5181854 0.48181462
## 3 1.0000000 4.359284e-09 0.9966386 0.0033613785 0.9227627 0.07723735
## 4 1.0000000 2.836982e-12 0.9824166 0.0175833907 0.9471394 0.05286061
## 5 0.9716647 2.833528e-02 0.7971119 0.2028880880 0.5261676 0.47383238
## 6 0.9999776 2.235629e-05 0.9819236 0.0180763533 0.7452732 0.25472679
## Class 5: 0 Class5: 1
## 1 0.9999993 6.600535e-07
## 2 0.2727579 7.272421e-01
## 3 0.6189105 3.810895e-01
## 4 0.9298631 7.013694e-02
## 5 0.9795013 2.049873e-02
## 6 0.9625821 3.741789e-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 1.664222e-04 0.5728740552 0.75784447 6.600535e-07
## 2 1.290483e-13 0.0001272975 0.48181462 7.272421e-01
## 3 4.359284e-09 0.0033613785 0.07723735 3.810895e-01
## 4 2.836982e-12 0.0175833907 0.05286061 7.013694e-02
## 5 2.833528e-02 0.2028880880 0.47383238 2.049873e-02
## 6 2.235629e-05 0.0180763533 0.25472679 3.741789e-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 1.664222e-04 5.728741e-01 7.578445e-01 6.600535e-07 4 4
## 2 1.290483e-13 1.272975e-04 4.818146e-01 7.272421e-01 5 5
## 3 4.359284e-09 3.361378e-03 7.723735e-02 3.810895e-01 5 5
## 4 2.836982e-12 1.758339e-02 5.286061e-02 7.013694e-02 5 4
## 5 2.833528e-02 2.028881e-01 4.738324e-01 2.049873e-02 4 4
## 6 2.235629e-05 1.807635e-02 2.547268e-01 3.741789e-02 4 4
## 7 3.518722e-12 8.356117e-06 6.559966e-02 3.407920e-04 4 5
## 8 1.176150e-08 3.051701e-01 9.802997e-01 6.498892e-07 4 3
## 9 1.482554e-11 7.887220e-10 3.253293e-02 9.964785e-01 5 5
## 10 6.855936e-04 2.797580e-04 9.172815e-05 2.373042e-03 5 3
## 11 3.004332e-13 2.590883e-03 3.113516e-01 8.376735e-01 5 4
## 12 9.834772e-04 1.963503e-02 1.015779e-02 4.977582e-02 5 3
## 13 7.611053e-05 1.367586e-02 5.776465e-02 7.886994e-01 5 5
## 14 5.478258e-03 1.531546e-02 3.239191e-01 2.104609e-03 4 4
## 15 1.157142e-03 2.615954e-01 2.780583e-01 7.923818e-03 4 4
## 16 2.461861e-09 5.424625e-03 2.208038e-03 9.799145e-01 5 5
## 17 5.994810e-09 3.066853e-05 7.918045e-02 5.210077e-01 5 4
## 18 4.621986e-05 1.333170e-02 7.131471e-01 4.032142e-01 4 5
## 19 8.072290e-04 5.578383e-01 2.558589e-01 6.447061e-03 3 4
## 20 5.838817e-09 7.247527e-02 6.127013e-01 2.392050e-01 4 2
## 21 2.463494e-14 1.775462e-03 7.291096e-01 8.603315e-04 4 4
## 22 1.025060e-11 1.667021e-03 2.404959e-01 9.355611e-01 5 4
## 23 2.850618e-04 9.965284e-01 5.715790e-03 4.575105e-06 3 3
## 24 3.873782e-12 1.643469e-05 4.363503e-01 9.607193e-01 5 5
## 25 2.642054e-06 8.051323e-02 7.375441e-01 4.880430e-02 4 4
## 26 1.100290e-07 3.222794e-02 5.746598e-02 8.062345e-01 5 5
## 27 3.164687e-08 6.997800e-04 7.029339e-01 1.755845e-01 4 4
## 28 1.813392e-05 2.412096e-02 4.115786e-02 5.679280e-01 5 5
## 29 6.305769e-08 1.960419e-04 8.150663e-01 1.728938e-04 4 3
## 30 9.521262e-01 7.005512e-02 4.448710e-03 9.088398e-07 2 3
## 31 2.668263e-09 1.580145e-05 1.599883e-02 9.977215e-01 5 5
## 32 9.051951e-11 1.063603e-03 8.316226e-04 9.939304e-01 5 5
## 33 1.370655e-11 6.468271e-04 8.385880e-01 6.572239e-01 4 5
## 34 5.998863e-08 9.435155e-01 1.216261e-02 5.213747e-03 3 3
## 35 1.894300e-06 4.844401e-01 4.093481e-01 5.942181e-02 3 4
## 36 6.097517e-07 4.961678e-01 6.192800e-01 6.389678e-02 4 4
## 37 1.439019e-02 5.883641e-02 9.826950e-01 1.916148e-05 4 5
## 38 5.923558e-01 9.996410e-01 9.566201e-01 6.512962e-08 3 3
## 39 1.882153e-14 1.224234e-08 2.026527e-03 9.996751e-01 5 5
## 40 8.992572e-05 4.636303e-01 5.660997e-01 7.025551e-05 4 3
## 41 2.037501e-12 1.605945e-05 1.196427e-03 9.997152e-01 5 5
## 42 2.770021e-09 9.611466e-03 9.505453e-01 6.371708e-05 4 4
## 43 6.695677e-13 1.356536e-08 7.809704e-01 1.112756e-03 4 5
## 44 1.175891e-05 2.720832e-01 7.386140e-01 5.557503e-02 4 3
## 45 1.352891e-07 6.421172e-01 9.070547e-01 1.201541e-02 4 5
## 46 5.286142e-03 1.511590e-01 5.615954e-01 2.929908e-02 4 4
## 47 9.106933e-06 9.859579e-02 3.506787e-01 5.571620e-01 5 5
## 48 6.194938e-07 1.862860e-01 4.729191e-01 2.964477e-01 4 3
## 49 2.337019e-09 6.049888e-05 1.249607e-02 9.990622e-01 5 5
## 50 4.272897e-07 1.895436e-03 7.019033e-02 9.438984e-01 5 5
## 51 3.832661e-09 2.372560e-04 2.580338e-02 9.962315e-01 5 4
## 52 6.116233e-06 3.850262e-03 6.874872e-01 2.498498e-01 4 5
## 53 8.061166e-10 7.645416e-03 2.876025e-02 9.757716e-01 5 4
## 54 7.127533e-10 9.572646e-04 6.013258e-01 8.201367e-01 5 4
## 55 3.471626e-05 7.376750e-01 1.791999e-01 1.372189e-01 3 4
## 56 2.053114e-08 2.082847e-04 1.016032e-02 9.968377e-01 5 5
## 57 6.582521e-07 4.293565e-04 5.201594e-02 9.898935e-01 5 2
## 58 3.713359e-08 1.538619e-04 1.450601e-02 9.973906e-01 5 5
## 59 1.337223e-08 1.224676e-03 4.169544e-02 9.905309e-01 5 4
## 60 4.289963e-04 9.048863e-01 8.295912e-01 1.056769e-03 3 4
## 61 1.710123e-06 6.179316e-04 4.316162e-02 9.865837e-01 5 5
## 62 2.172837e-08 2.158865e-03 9.199262e-02 9.693999e-01 5 5
## 63 3.732034e-04 8.713847e-02 8.143391e-01 9.987295e-02 4 4
## 64 1.663896e-09 1.627788e-04 1.329512e-01 9.827814e-01 5 5
## 65 2.116548e-11 6.618289e-06 6.083304e-03 9.995622e-01 5 5
## 66 2.212663e-08 6.655557e-07 1.440649e-02 9.993368e-01 5 5
## 67 1.854794e-06 4.991737e-04 5.249063e-02 9.395999e-01 5 3
## 68 1.255370e-05 1.074072e-01 2.944856e-01 5.367225e-01 5 3
## 69 2.433875e-08 8.589953e-02 6.390010e-01 2.193409e-01 4 4
## 70 5.526712e-10 4.599038e-04 7.431414e-01 4.343030e-01 4 4
## 71 9.350939e-08 2.452599e-05 1.130688e-02 9.984977e-01 5 5
## 72 1.115971e-08 2.626240e-05 5.749996e-02 9.887611e-01 5 5
## 73 8.599804e-05 6.649603e-02 2.682493e-01 5.518569e-01 5 2
## 74 2.537415e-04 1.989375e-01 2.274104e-01 3.318735e-01 5 4
## 75 1.263894e-08 7.275141e-05 6.657376e-01 8.158829e-01 5 4
## 76 1.400826e-12 1.013989e-03 7.593794e-01 5.327967e-01 4 5
## 77 2.894640e-13 3.456931e-04 4.346460e-03 3.482077e-01 5 3
## 78 7.264887e-13 9.445731e-03 5.362690e-01 6.622657e-01 5 4
## 79 2.160751e-11 1.093268e-02 3.478725e-02 1.701681e-03 4 5
## 80 3.202717e-08 3.426395e-06 1.057566e-02 9.978311e-01 5 5
## 81 9.862520e-13 2.402407e-05 4.682141e-01 7.633027e-01 5 5
## 82 3.985211e-14 9.400079e-04 4.308184e-02 9.378520e-01 5 4
## 83 1.206637e-15 4.495061e-05 5.653814e-02 1.071586e-03 4 5
## 84 5.856958e-10 2.839916e-04 2.263324e-03 9.982351e-01 5 5
## 85 3.305795e-09 1.320794e-02 2.615547e-01 3.316648e-01 5 5
## 86 4.191955e-10 8.482901e-01 9.892196e-01 3.168121e-06 4 3
## 87 1.858928e-04 8.609202e-01 6.016760e-01 9.889085e-03 3 4
## 88 3.584052e-06 6.131987e-02 2.824482e-02 2.925305e-03 3 5
## 89 9.830229e-17 1.106068e-07 4.975255e-03 9.999457e-01 5 5
## 90 3.686010e-09 1.920847e-04 2.560165e-01 7.489103e-01 5 3
## 91 2.215050e-09 8.175437e-02 1.332975e-01 7.790387e-01 5 3
## 92 4.742891e-10 5.970742e-02 6.092829e-01 1.030052e-01 4 5
## 93 5.012155e-13 8.246791e-04 9.689189e-01 3.311372e-03 4 4
## 94 6.845863e-10 3.355418e-10 6.945716e-03 4.893697e-01 5 5
## 95 5.727310e-08 1.027700e-04 1.476685e-01 8.686586e-01 5 5
## 96 8.313750e-19 1.491203e-05 6.117078e-02 9.968672e-01 5 5
## 97 2.563272e-11 5.221618e-05 5.636455e-02 9.876750e-01 5 5
## 98 2.511824e-10 3.060590e-01 1.908686e-03 9.436137e-01 5 5
## 99 7.259267e-02 4.025603e-03 3.299888e-01 2.331114e-01 4 2
## 100 1.239166e-06 3.143234e-03 4.825716e-01 1.415901e-01 4 5CM <- table(Evaluation$Actual,Evaluation$Vote)#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] 0.6057692Precision## 2
## 0.6057692Recall## 2
## 0.5614345