setwd("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TP/50")#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$Score#Import Features
Features <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TP/50/Feature Set 1: 50th TP.csv")
Features <- Features[-1]#Class 2
Label2 <- list()
for(i in 1:1000){
if(Label[i]==3| Label[i]==4){
Label2[i] <- 1
}else{
Label2[i] <- 0
}
}#As Factor
Label2 <- as.factor(unlist(Label2))#Class 3
Label3 <- list()
for(i in 1:1000){
if(Label[i]==5| Label[i]==6){
Label3[i] <- 1
}else{
Label3[i] <- 0
}
}#As Factor
Label3 <- as.factor(unlist(Label3))#Class 4
Label4 <- list()
for(i in 1:1000){
if(Label[i]==7| Label[i]==8){
Label4[i] <- 1
}else{
Label4[i] <- 0
}
}#As Factor
Label4 <- as.factor(unlist(Label4))#Class 5
Label5 <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
Label5[i] <- 1
}else{
Label5[i] <- 0
}
}#As Factor
Label5 <- as.factor(unlist(Label5))#All Labels
All <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
All[i] <- 5
}else if(Label[i]==7| Label[i]==8){
All[i] <- 4
}else if(Label[i]==5| Label[i]==6){
All[i] <- 3
}else{
All[i] <- 2
}
}#As Factor
All <- as.factor(unlist(All))#Control
Control.df <- data.frame(matrix(seq(1,1000),ncol=1,nrow=1000))
Control.df$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 1:1101){
Features[,i] <- as.factor(Features[,i])
}
str(Features)## 'data.frame': 1000 obs. of 1101 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 ...
## $ 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 ...
## $ accommod : 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 ...
## $ across : 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 ...
## $ 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 ...
## $ 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 ...
## $ 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 ...
## $ 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 ...
## $ 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 ...
## $ 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 ...
## $ all : 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 ...
## $ 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 ...
## $ 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 ...
## $ amen : Factor w/ 2 levels "0","1": 1 2 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 ...
## $ 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 ...
## $ anywh : 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 ...
## $ apolog : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ appear : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ appoint : 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 ...
## $ approach : 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 ...
## $ arrang : Factor w/ 2 levels "0","1": 1 1 1 1 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 ...
## $ assum : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ atm : 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 ...
## $ attend : 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 ...
## $ attic : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ attitud : 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 ...
## $ avoid : 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 ...
## $ bake : 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 ...
## $ base : 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 ...
## $ bathtub : 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 ...
## $ begin : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ behind : 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 ...
## $ benefit : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ besid : 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 ...
## [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 Class4: 1
## 1 1.0000000 4.681638e-14 0.1642978 8.357022e-01 0.00222148 9.977785e-01
## 2 1.0000000 2.106918e-14 0.9999999 1.388791e-07 0.99999005 9.948552e-06
## 3 1.0000000 1.988157e-16 0.9999957 4.261527e-06 0.99948388 5.161225e-04
## 4 1.0000000 2.292304e-11 0.9999999 1.432520e-07 0.99954769 4.523123e-04
## 5 0.9991637 8.362607e-04 0.8812187 1.187813e-01 0.91330439 8.669561e-02
## 6 1.0000000 1.552615e-11 0.9340747 6.592533e-02 0.99479980 5.200200e-03
## Class 5: 0 Class5: 1
## 1 1.0000000000 3.140330e-13
## 2 0.0001189609 9.998810e-01
## 3 0.0023075825 9.976924e-01
## 4 0.9911207947 8.879205e-03
## 5 0.9938771367 6.122863e-03
## 6 0.9583627093 4.163729e-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 4.681638e-14 8.357022e-01 9.977785e-01 3.140330e-13
## 2 2.106918e-14 1.388791e-07 9.948552e-06 9.998810e-01
## 3 1.988157e-16 4.261527e-06 5.161225e-04 9.976924e-01
## 4 2.292304e-11 1.432520e-07 4.523123e-04 8.879205e-03
## 5 8.362607e-04 1.187813e-01 8.669561e-02 6.122863e-03
## 6 1.552615e-11 6.592533e-02 5.200200e-03 4.163729e-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 4.681638e-14 8.357022e-01 9.977785e-01 3.140330e-13 4 4
## 2 2.106918e-14 1.388791e-07 9.948552e-06 9.998810e-01 5 5
## 3 1.988157e-16 4.261527e-06 5.161225e-04 9.976924e-01 5 5
## 4 2.292304e-11 1.432520e-07 4.523123e-04 8.879205e-03 5 4
## 5 8.362607e-04 1.187813e-01 8.669561e-02 6.122863e-03 3 4
## 6 1.552615e-11 6.592533e-02 5.200200e-03 4.163729e-02 3 4
## 7 2.131027e-13 1.764076e-12 8.220251e-04 3.025275e-07 4 5
## 8 3.766895e-12 5.623745e-01 9.985215e-01 1.333606e-12 4 3
## 9 1.094747e-15 1.879874e-09 1.383624e-01 8.558471e-01 5 5
## 10 1.200136e-09 9.646900e-01 2.876432e-06 3.720917e-06 3 3
## 11 1.334894e-17 4.858212e-06 7.701304e-01 1.506605e-01 4 4
## 12 1.766475e-08 2.050708e-03 8.984598e-01 9.734475e-03 4 3
## 13 3.013407e-13 2.861383e-03 1.076583e-03 9.736094e-01 5 5
## 14 1.454746e-06 4.484529e-01 6.820138e-01 9.870050e-07 4 4
## 15 3.942778e-06 8.661579e-01 1.670202e-01 5.736632e-04 3 4
## 16 4.305362e-19 2.482141e-08 1.360373e-01 2.825346e-02 4 5
## 17 2.849714e-13 4.817707e-04 9.013906e-03 1.432289e-01 5 4
## 18 1.581454e-10 5.459085e-07 1.016094e-01 9.757097e-01 5 5
## 19 1.341248e-08 4.256620e-01 6.872899e-01 2.940232e-04 4 4
## 20 1.697986e-09 7.997032e-04 2.481593e-01 4.842759e-01 5 2
## 21 2.473312e-15 1.763178e-03 9.839232e-01 4.839484e-08 4 4
## 22 1.246890e-23 2.837634e-04 7.640499e-01 1.764667e-01 4 4
## 23 9.122437e-04 9.996500e-01 8.472197e-01 8.368209e-13 3 3
## 24 1.555781e-18 2.999362e-06 8.709814e-02 9.990686e-01 5 5
## 25 2.088023e-10 2.938176e-01 2.375239e-01 1.896047e-02 3 4
## 26 6.485952e-11 1.037708e-02 1.522109e-01 4.207420e-01 5 5
## 27 3.984256e-11 2.012342e-07 2.158521e-01 8.990195e-01 5 4
## 28 1.974274e-08 1.142715e-04 1.347663e-03 9.183845e-01 5 5
## 29 4.279990e-02 3.178032e-02 1.578215e-01 8.082322e-09 4 3
## 30 1.502609e-06 8.758927e-01 1.589524e-01 1.648647e-16 3 3
## 31 4.492049e-13 4.714345e-06 1.331986e-03 9.993816e-01 5 5
## 32 8.283139e-13 6.612399e-04 9.500400e-05 9.948149e-01 5 5
## 33 2.573422e-16 7.216928e-05 9.780297e-01 5.762097e-01 4 5
## 34 6.886083e-12 9.688154e-01 6.375186e-01 2.573212e-06 3 3
## 35 5.073665e-11 9.268767e-01 2.040858e-01 1.303684e-03 3 4
## 36 4.852738e-14 9.445430e-01 5.461624e-01 7.500447e-03 3 4
## 37 9.407721e-07 2.231033e-05 9.992050e-01 3.218797e-10 4 5
## 38 9.999987e-01 9.888821e-01 6.036642e-01 9.329903e-10 2 3
## 39 2.114435e-16 2.100618e-06 2.508057e-04 9.992014e-01 5 5
## 40 2.126797e-09 2.048950e-02 9.983999e-01 4.438910e-09 4 3
## 41 3.450157e-14 2.021298e-06 4.804179e-04 9.999208e-01 5 5
## 42 1.446981e-16 9.464644e-01 9.199854e-01 2.471510e-05 3 4
## 43 2.091485e-10 7.688282e-14 8.109569e-01 4.655424e-06 4 5
## 44 1.939424e-09 4.847311e-04 9.132652e-01 1.208240e-02 4 3
## 45 6.857225e-10 3.064735e-02 9.802320e-01 1.218307e-02 4 5
## 46 5.359538e-06 1.176892e-02 8.785298e-01 9.493935e-03 4 4
## 47 5.265192e-11 9.466489e-05 9.453038e-01 3.275760e-01 4 5
## 48 8.372142e-10 5.084725e-01 1.093426e-01 1.291813e-01 3 3
## 49 6.360004e-13 4.044592e-06 7.861004e-03 9.999293e-01 5 5
## 50 9.784224e-08 3.750654e-05 5.023479e-03 9.494786e-01 5 5
## 51 1.081971e-13 2.481127e-05 1.021157e-02 9.998184e-01 5 4
## 52 1.192695e-10 1.262977e-05 9.954094e-01 2.608489e-02 4 5
## 53 3.392280e-12 1.901116e-03 9.325566e-03 9.865416e-01 5 4
## 54 3.651243e-13 3.860024e-06 9.564208e-01 4.347578e-01 4 4
## 55 2.125522e-11 3.182206e-02 1.589717e-01 6.845065e-01 5 4
## 56 7.658369e-13 4.798037e-06 4.727616e-04 9.999785e-01 5 5
## 57 2.103074e-11 3.932798e-05 2.179182e-02 9.995113e-01 5 2
## 58 8.885809e-14 3.298720e-07 1.350666e-02 9.998764e-01 5 5
## 59 3.335603e-14 8.631910e-06 7.889546e-03 9.999043e-01 5 4
## 60 1.520254e-10 4.932602e-01 9.394971e-01 4.374855e-03 4 4
## 61 5.809815e-11 5.740931e-05 1.936981e-02 9.993058e-01 5 5
## 62 5.311426e-12 5.334818e-04 7.444666e-04 9.991241e-01 5 5
## 63 3.822089e-08 1.886866e-02 6.628443e-01 4.768663e-01 4 4
## 64 1.189238e-14 5.943545e-06 3.712372e-01 9.942570e-01 5 5
## 65 2.484947e-15 8.730998e-07 2.552200e-03 9.999706e-01 5 5
## 66 1.358437e-13 5.690710e-09 1.888850e-03 9.999943e-01 5 5
## 67 2.740610e-08 2.599625e-05 8.332379e-02 9.302090e-01 5 3
## 68 3.373429e-09 3.442522e-02 1.484083e-01 8.083588e-01 5 3
## 69 1.626063e-12 7.845565e-05 1.725125e-01 9.717174e-01 5 4
## 70 4.827305e-17 6.136196e-07 2.267230e-01 9.858989e-01 5 4
## 71 1.746346e-12 1.009101e-06 7.245795e-03 9.999272e-01 5 5
## 72 2.377473e-14 5.230791e-07 3.388618e-01 9.937997e-01 5 5
## 73 6.074262e-08 5.463495e-03 2.044941e-01 7.274661e-01 5 2
## 74 1.299787e-07 4.537355e-03 1.281373e-01 7.983273e-01 5 4
## 75 7.819261e-13 5.749192e-06 4.862032e-01 9.866838e-01 5 4
## 76 2.602698e-14 5.485119e-06 6.369956e-01 3.443817e-01 4 5
## 77 2.112658e-14 1.575228e-06 1.374151e-02 2.083542e-03 4 3
## 78 1.191138e-16 2.068812e-04 6.137568e-01 5.107603e-01 4 4
## 79 2.659123e-20 8.892096e-01 9.654198e-01 3.202421e-09 4 5
## 80 1.582601e-13 3.164933e-09 1.307821e-03 9.999870e-01 5 5
## 81 3.647100e-17 4.800097e-07 1.288152e-01 9.834594e-01 5 5
## 82 2.644532e-17 1.012112e-05 6.351110e-02 9.991626e-01 5 4
## 83 3.888489e-18 1.360730e-09 1.059236e-01 1.891266e-07 4 5
## 84 7.955353e-12 1.374444e-05 1.484232e-03 9.952110e-01 5 5
## 85 7.070890e-14 1.783744e-03 3.103819e-01 4.752757e-01 5 5
## 86 7.521320e-15 1.220565e-04 2.349044e-02 4.477208e-06 4 3
## 87 5.835437e-01 3.446675e-03 8.550242e-01 2.662451e-09 4 4
## 88 1.140102e-12 1.838280e-04 3.600268e-03 1.468946e-02 5 5
## 89 6.553860e-21 7.214720e-07 2.296139e-02 9.996505e-01 5 5
## 90 5.386032e-12 4.248122e-05 2.116565e-01 2.911687e-02 4 3
## 91 1.196239e-13 1.102389e-01 3.739355e-01 5.809481e-01 5 3
## 92 1.079870e-14 4.513949e-02 3.087539e-01 6.800677e-02 4 5
## 93 2.598400e-18 2.281177e-03 9.961089e-01 1.844758e-04 4 4
## 94 2.219248e-14 1.216840e-12 1.544977e-03 5.197920e-04 4 5
## 95 4.909426e-10 3.240547e-04 3.953398e-04 9.515759e-01 5 5
## 96 1.433203e-22 8.317325e-07 4.041837e-01 9.920590e-01 5 5
## 97 7.363209e-18 1.746500e-05 2.724720e-02 9.976688e-01 5 5
## 98 5.281205e-13 5.184803e-05 1.979216e-03 9.905448e-01 5 5
## 99 2.454953e-02 9.369386e-03 1.773149e-03 5.397619e-02 5 2
## 100 2.021557e-12 4.127947e-02 5.935884e-01 1.695911e-01 4 5CM <- table(Evaluation$Actual,Evaluation$Vote)
CM##
## 2 3 4 5
## 2 0 0 2 5
## 3 1 7 10 7
## 4 0 9 26 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] 0.5961538Precision## 2
## 0.5961538Recall## 2
## 0.5617512