setwd("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TP/70")#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$Score#Import Features
Features <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TP/70/Feature Set 1: 70th TP.csv")
Features <- Features[-1]#Class 2
Label2 <- list()
for(i in 1:1000){
if(Label[i]==3| Label[i]==4){
Label2[i] <- 1
}else{
Label2[i] <- 0
}
}#As Factor
Label2 <- as.factor(unlist(Label2))#Class 3
Label3 <- list()
for(i in 1:1000){
if(Label[i]==5| Label[i]==6){
Label3[i] <- 1
}else{
Label3[i] <- 0
}
}#As Factor
Label3 <- as.factor(unlist(Label3))#Class 4
Label4 <- list()
for(i in 1:1000){
if(Label[i]==7| Label[i]==8){
Label4[i] <- 1
}else{
Label4[i] <- 0
}
}#As Factor
Label4 <- as.factor(unlist(Label4))#Class 5
Label5 <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
Label5[i] <- 1
}else{
Label5[i] <- 0
}
}#As Factor
Label5 <- as.factor(unlist(Label5))#All Labels
All <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
All[i] <- 5
}else if(Label[i]==7| Label[i]==8){
All[i] <- 4
}else if(Label[i]==5| Label[i]==6){
All[i] <- 3
}else{
All[i] <- 2
}
}#As Factor
All <- as.factor(unlist(All))#Control
Control.df <- data.frame(matrix(seq(1,1000),ncol=1,nrow=1000))
Control.df$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 1:761){
Features[,i] <- as.factor(Features[,i])
}
str(Features)## 'data.frame': 1000 obs. of 761 variables:
## $ abl : Factor w/ 2 levels "0","1": 2 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 ...
## $ access : 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 ...
## $ actual : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
## $ adequ : 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 ...
## $ adverti : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
## $ advic : 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 ...
## $ after : Factor w/ 2 levels "0","1": 1 1 1 1 2 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 ...
## $ all : 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 ...
## $ alreadi : Factor w/ 2 levels "0","1": 1 1 1 2 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 ...
## $ amen : Factor w/ 2 levels "0","1": 1 2 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 ...
## $ 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 ...
## $ answer : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ anymor : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ anyon : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
## $ anyth : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ anyway : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ apart : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 1 ...
## $ appear : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ appreci : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ architectur : Factor w/ 2 levels "0","1": 1 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 ...
## $ arena : Factor w/ 2 levels "0","1": 1 1 1 1 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 ...
## $ aspect : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ atmosph : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ attent : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ attract : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ avail : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ averag : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ awar : Factor w/ 2 levels "0","1": 1 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 ...
## $ awesom : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...
## $ back : Factor w/ 2 levels "0","1": 1 2 2 1 1 1 1 1 1 1 ...
## $ bacon : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ bad : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ...
## $ bag : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ bang : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ bank : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ bar : Factor w/ 2 levels "0","1": 1 1 2 2 1 1 1 1 1 1 ...
## $ bare : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ basement : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ basic : Factor w/ 2 levels "0","1": 1 1 2 1 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 ...
## $ beer : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ believ : 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 ...
## $ bigger : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ bike : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ birthday : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
## $ bit : Factor w/ 2 levels "0","1": 1 2 2 1 1 1 1 1 1 1 ...
## $ black : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ block : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ board : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ bonus : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ book : Factor w/ 2 levels "0","1": 2 1 1 1 2 1 1 1 1 1 ...
## $ boutiqu : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ box : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ bread : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ breakfast : Factor w/ 2 levels "0","1": 1 1 2 1 1 1 1 2 1 2 ...
## $ bright : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 1 1 1 ...
## $ brilliant : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ broken : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ brought : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 ...
## $ buy : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ cafe : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ call : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ came : Factor w/ 2 levels "0","1": 2 1 1 2 1 1 1 1 1 1 ...
## $ can : Factor w/ 2 levels "0","1": 2 2 1 1 1 1 1 1 1 1 ...
## $ car : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ card : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ care : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ carpet : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ case : 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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## 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
## 1 1.0000000 7.092042e-14 0.004322569 9.956774e-01 0.008680259
## 2 1.0000000 2.553354e-12 0.999997910 2.090433e-06 0.999961652
## 3 1.0000000 5.536907e-14 0.999994166 5.833717e-06 0.996027459
## 4 1.0000000 3.220988e-11 0.999999935 6.470192e-08 0.996856503
## 5 0.9830366 1.696340e-02 0.800747708 1.992523e-01 0.893247747
## 6 1.0000000 1.260284e-09 0.992195811 7.804189e-03 0.994072232
## Class4: 1 Class 5: 0 Class5: 1
## 1 9.913197e-01 1.000000000 1.907830e-12
## 2 3.834764e-05 0.002459354 9.975406e-01
## 3 3.972541e-03 0.016961647 9.830384e-01
## 4 3.143497e-03 0.990686954 9.313046e-03
## 5 1.067523e-01 0.997308742 2.691258e-03
## 6 5.927768e-03 0.862458766 1.375412e-01Transformed.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 7.092042e-14 9.956774e-01 9.913197e-01 1.907830e-12
## 2 2.553354e-12 2.090433e-06 3.834764e-05 9.975406e-01
## 3 5.536907e-14 5.833717e-06 3.972541e-03 9.830384e-01
## 4 3.220988e-11 6.470192e-08 3.143497e-03 9.313046e-03
## 5 1.696340e-02 1.992523e-01 1.067523e-01 2.691258e-03
## 6 1.260284e-09 7.804189e-03 5.927768e-03 1.375412e-01Evaluation <- 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 7.092042e-14 9.956774e-01 9.913197e-01 1.907830e-12 3 4
## 2 2.553354e-12 2.090433e-06 3.834764e-05 9.975406e-01 5 5
## 3 5.536907e-14 5.833717e-06 3.972541e-03 9.830384e-01 5 5
## 4 3.220988e-11 6.470192e-08 3.143497e-03 9.313046e-03 5 4
## 5 1.696340e-02 1.992523e-01 1.067523e-01 2.691258e-03 3 4
## 6 1.260284e-09 7.804189e-03 5.927768e-03 1.375412e-01 5 4
## 7 7.058768e-12 1.119016e-12 9.616217e-03 3.130544e-07 4 5
## 8 3.459460e-12 3.672553e-01 9.998263e-01 1.938182e-12 4 3
## 9 5.916396e-14 4.239338e-10 8.376455e-02 9.354344e-01 5 5
## 10 1.158666e-08 5.141286e-01 2.718587e-04 3.499334e-06 3 3
## 11 1.935688e-16 1.892020e-05 8.953463e-01 3.166573e-01 4 4
## 12 4.767076e-07 5.359598e-03 7.326514e-01 1.181948e-02 4 3
## 13 1.628551e-11 1.484712e-02 1.661892e-04 9.890143e-01 5 5
## 14 3.925687e-05 6.807242e-01 3.991353e-01 1.200938e-06 3 4
## 15 2.766438e-06 5.933963e-01 4.818108e-01 1.398742e-03 3 4
## 16 6.829357e-17 5.307057e-07 1.800140e-02 2.124201e-02 5 5
## 17 5.875427e-12 8.890105e-04 1.132202e-02 6.823045e-02 5 4
## 18 8.546735e-09 1.231089e-07 6.049606e-02 9.899013e-01 5 5
## 19 2.765331e-07 5.777334e-01 7.345423e-01 1.288123e-04 4 4
## 20 9.102183e-10 2.094289e-03 4.330704e-01 5.332673e-01 5 2
## 21 8.192544e-14 1.183448e-03 9.600368e-01 7.043153e-07 4 4
## 22 8.820201e-22 5.504842e-06 7.721540e-01 5.922541e-01 4 4
## 23 1.306723e-02 9.988877e-01 5.159674e-01 4.099341e-11 3 3
## 24 3.207647e-17 5.536959e-06 1.072370e-01 9.978763e-01 5 5
## 25 4.305003e-09 4.344136e-01 2.817111e-01 8.394699e-03 3 4
## 26 5.264755e-09 4.713787e-03 1.699922e-01 4.325177e-01 5 5
## 27 8.214579e-10 3.714883e-07 2.573684e-01 7.959075e-01 5 4
## 28 4.070478e-07 2.109305e-04 1.696107e-03 8.313365e-01 5 5
## 29 7.282404e-03 7.265439e-03 7.438017e-02 7.311597e-07 4 3
## 30 4.182939e-04 3.561368e-01 2.662037e-01 3.108166e-16 3 3
## 31 3.646270e-11 2.129311e-06 1.519173e-03 9.994107e-01 5 5
## 32 4.440240e-13 1.732065e-03 2.198429e-04 9.957346e-01 5 5
## 33 6.944737e-15 1.892223e-04 9.323737e-01 6.232618e-01 4 5
## 34 1.465150e-09 9.078338e-01 8.471482e-01 3.225144e-06 3 3
## 35 1.079522e-08 9.421551e-01 1.168208e-01 1.633438e-03 3 4
## 36 1.309579e-12 9.780998e-01 7.358086e-01 1.421384e-03 3 4
## 37 1.188815e-05 1.225005e-04 9.991598e-01 1.121145e-09 4 5
## 38 9.999631e-01 9.827778e-01 7.615588e-01 9.512981e-09 2 3
## 39 4.359458e-15 3.877839e-06 3.157423e-04 9.981787e-01 5 5
## 40 7.044756e-08 8.780011e-01 5.215496e-01 4.796434e-07 3 3
## 41 7.113395e-13 3.731410e-06 6.047679e-04 9.998192e-01 5 5
## 42 2.682986e-14 4.937854e-01 5.958025e-01 9.324819e-04 4 4
## 43 6.643910e-08 3.292336e-12 2.132677e-01 8.085145e-06 4 5
## 44 1.404309e-08 3.101458e-04 9.375176e-01 1.057674e-01 4 3
## 45 5.566123e-08 1.996314e-01 9.335905e-01 1.277626e-02 4 5
## 46 1.446144e-04 3.028265e-02 6.913568e-01 1.152802e-02 4 4
## 47 4.273844e-09 7.463343e-04 8.305016e-01 3.382669e-01 4 5
## 48 1.726135e-08 6.563209e-01 1.338706e-01 6.101472e-02 3 3
## 49 1.311280e-11 7.466494e-06 9.876847e-03 9.998387e-01 5 5
## 50 4.678680e-09 4.819407e-05 2.077276e-02 9.880093e-01 5 5
## 51 2.230766e-12 4.580189e-05 1.282239e-02 9.995854e-01 5 4
## 52 9.681301e-09 9.962887e-05 9.569033e-01 8.951254e-02 4 5
## 53 6.994068e-11 3.503919e-03 1.171254e-02 9.697968e-01 5 4
## 54 1.973260e-11 8.704836e-07 9.258973e-01 6.524094e-01 4 4
## 55 4.382317e-10 5.720497e-02 1.922297e-01 4.872757e-01 5 4
## 56 2.066717e-11 1.258146e-05 1.093445e-03 9.998860e-01 5 5
## 57 4.336034e-10 7.259901e-05 2.728179e-02 9.988851e-01 5 2
## 58 1.832041e-12 6.089600e-07 1.694552e-02 9.997179e-01 5 5
## 59 6.877213e-13 1.593482e-05 9.912635e-03 9.997816e-01 5 4
## 60 8.215987e-09 8.363932e-01 8.983795e-01 2.293719e-03 4 4
## 61 1.197844e-09 1.059753e-04 2.426475e-02 9.984165e-01 5 5
## 62 1.095089e-10 9.843897e-04 9.370982e-04 9.980027e-01 5 5
## 63 1.031443e-06 4.800847e-02 8.198179e-01 1.465351e-01 4 4
## 64 6.427062e-13 3.121424e-05 2.515773e-01 9.890805e-01 5 5
## 65 5.123365e-14 1.611785e-06 3.211082e-03 9.999330e-01 5 5
## 66 2.800772e-12 1.050534e-08 2.376888e-03 9.999870e-01 5 5
## 67 5.650478e-07 4.798932e-05 1.026881e-01 8.537644e-01 5 3
## 68 9.103665e-08 8.549625e-02 2.874050e-01 4.427381e-01 5 3
## 69 3.352551e-11 1.448234e-04 2.079031e-01 9.376930e-01 5 4
## 70 2.608845e-15 3.222673e-06 1.430466e-01 9.733904e-01 5 4
## 71 3.600546e-11 1.862849e-06 9.105324e-03 9.998339e-01 5 5
## 72 1.284870e-12 2.747163e-06 2.258883e-01 9.882159e-01 5 5
## 73 1.252365e-06 1.003948e-02 2.445071e-01 5.390047e-01 5 2
## 74 1.826371e-07 3.471066e-02 1.435826e-01 8.059670e-01 5 4
## 75 1.612144e-11 1.061324e-05 5.436664e-01 9.701104e-01 5 4
## 76 5.366139e-13 1.012576e-05 6.884023e-01 1.870496e-01 4 5
## 77 5.701300e-13 4.130600e-06 4.296675e-03 2.534006e-03 4 3
## 78 9.668663e-15 9.345162e-05 9.171733e-01 2.141289e-01 4 4
## 79 5.476249e-20 3.084944e-01 9.636016e-01 7.202425e-08 4 5
## 80 4.270868e-12 8.299183e-09 4.054145e-04 9.999893e-01 5 5
## 81 7.519445e-16 8.861216e-07 1.569416e-01 9.630232e-01 5 5
## 82 5.452391e-16 1.868393e-05 7.866610e-02 9.980903e-01 5 4
## 83 1.432199e-17 3.054038e-08 4.097622e-03 3.867484e-06 4 5
## 84 1.640203e-10 2.537263e-05 1.867921e-03 9.891337e-01 5 5
## 85 1.457848e-12 3.287919e-03 3.616935e-01 2.840529e-01 4 5
## 86 1.439201e-11 1.903428e-03 3.139415e-04 1.037139e-04 3 3
## 87 7.515729e-03 1.808915e-01 9.488324e-01 3.033155e-07 4 4
## 88 6.358562e-10 5.038494e-04 6.818441e-03 4.628172e-03 4 5
## 89 1.351249e-19 1.331873e-06 2.873735e-02 9.992025e-01 5 5
## 90 1.826201e-11 1.320533e-05 2.404765e-01 6.771017e-01 5 3
## 91 9.710067e-12 5.299449e-02 4.052377e-01 5.926204e-01 5 3
## 92 2.226433e-13 8.026443e-02 3.599368e-01 3.097271e-02 4 5
## 93 6.255129e-18 4.627985e-02 9.924845e-01 3.201943e-04 4 4
## 94 9.543779e-15 9.561886e-12 1.003635e-01 9.945054e-05 4 5
## 95 1.012206e-08 5.980571e-04 4.976794e-04 8.959166e-01 5 5
## 96 2.954920e-21 1.535419e-06 4.606440e-01 9.820539e-01 5 5
## 97 1.518117e-16 3.224079e-05 3.406368e-02 9.946939e-01 5 5
## 98 2.854149e-11 2.722417e-04 9.112380e-03 9.077759e-01 5 5
## 99 3.416249e-01 1.716033e-02 2.231359e-03 2.438279e-02 2 2
## 100 2.863749e-10 2.688139e-02 2.732684e-01 3.731481e-01 5 5CM <- table(Evaluation$Actual,Evaluation$Vote)
CM##
## 2 3 4 5
## 2 1 0 2 4
## 3 1 9 7 8
## 4 0 8 26 28
## 5 0 3 18 93#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] 0.6201923Precision## 2
## 0.6201923Recall## 2
## 0.6003816