setwd("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TF/70")#install.packages("naivebayes")
library(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$Score#Import Features
Features <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/2.Feature Set 1/TF/70/Feature Set 1 70th Percentile.csv")
Features <- Features[-1]#Class 2
Label2 <- list()
for(i in 1:1000){
if(Label[i]==3| Label[i]==4){
Label2[i] <- 1
}else{
Label2[i] <- 0
}
}#As Factor
Label2 <- as.factor(unlist(Label2))#Class 3
Label3 <- list()
for(i in 1:1000){
if(Label[i]==5| Label[i]==6){
Label3[i] <- 1
}else{
Label3[i] <- 0
}
}#As Factor
Label3 <- as.factor(unlist(Label3))#Class 4
Label4 <- list()
for(i in 1:1000){
if(Label[i]==7| Label[i]==8){
Label4[i] <- 1
}else{
Label4[i] <- 0
}
}#As Factor
Label4 <- as.factor(unlist(Label4))#Class 5
Label5 <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
Label5[i] <- 1
}else{
Label5[i] <- 0
}
}#As Factor
Label5 <- as.factor(unlist(Label5))#All Labels
All <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
All[i] <- 5
}else if(Label[i]==7| Label[i]==8){
All[i] <- 4
}else if(Label[i]==5| Label[i]==6){
All[i] <- 3
}else{
All[i] <- 2
}
}#As Factor
All <- as.factor(unlist(All))#Control
Control.df <- data.frame(matrix(seq(1,1000),ncol=1,nrow=1000))
Control.df$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 1:784){
Features[,i] <- as.factor(Features[,i])
}
str(Features)## 'data.frame': 1000 obs. of 784 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/ 3 levels "0","1","2": 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 ...
## $ adult : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ advanc : Factor w/ 3 levels "0","1","2": 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/ 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 ...
## $ all : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ alloc : Factor w/ 3 levels "0","1","2": 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/ 3 levels "0","1","2": 1 1 1 2 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 ...
## $ amen : Factor w/ 2 levels "0","1": 1 2 1 1 1 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 ...
## $ annoy : Factor w/ 3 levels "0","1","2": 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/ 3 levels "0","1","2": 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/ 3 levels "0","1","2": 1 2 1 1 1 1 1 1 1 1 ...
## $ anyth : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ anyway : Factor w/ 3 levels "0","1","2": 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/ 3 levels "0","1","2": 1 1 1 2 1 1 1 1 1 1 ...
## $ arena : Factor w/ 3 levels "0","1","2": 1 1 1 1 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 ...
## $ 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/ 3 levels "0","1","2": 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/ 4 levels "0","1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ awesom : Factor w/ 3 levels "0","1","3": 1 1 1 1 1 2 1 1 1 1 ...
## $ back : Factor w/ 4 levels "0","1","2","3": 1 2 2 1 1 1 1 1 1 1 ...
## $ bacon : Factor w/ 3 levels "0","1","2": 1 1 1 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 ...
## $ bag : Factor w/ 3 levels "0","1","2": 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/ 3 levels "0","1","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ bar : Factor w/ 3 levels "0","1","2": 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/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ basic : Factor w/ 3 levels "0","1","2": 1 1 2 1 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 ...
## $ beer : Factor w/ 3 levels "0","1","2": 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/ 3 levels "0","1","2": 3 1 1 1 1 1 1 1 1 3 ...
## $ bigger : Factor w/ 3 levels "0","1","2": 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/ 3 levels "0","1","2": 1 1 1 1 2 1 1 1 1 1 ...
## $ bit : Factor w/ 3 levels "0","1","2": 1 3 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 ...
## $ boiler : Factor w/ 3 levels "0","1","2": 1 1 2 1 1 1 1 1 1 1 ...
## $ bonus : Factor w/ 3 levels "0","1","2": 1 1 1 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 ...
## $ boutiqu : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ box : Factor w/ 3 levels "0","1","2": 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/ 6 levels "0","1","2","3",..: 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/ 3 levels "0","1","2": 2 1 1 1 1 1 1 1 1 1 ...
## $ brought : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 ...
## $ buy : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ cab : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ cafe : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ call : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ came : Factor w/ 3 levels "0","1","2": 2 1 1 3 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 ...
## $ car : Factor w/ 3 levels "0","1","3": 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
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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 6.001452e-15 0.009305906 9.906941e-01 0.04496049 0.9550395125
## 2 1.0000000 3.181332e-17 0.999999260 7.398858e-07 0.99996086 0.0000391401
## 3 1.0000000 6.037856e-13 0.999658587 3.414132e-04 0.97094131 0.0290586887
## 4 1.0000000 6.676399e-12 0.999999944 5.630880e-08 0.99888244 0.0011175627
## 5 0.9989019 1.098094e-03 0.901515478 9.848452e-02 0.81943240 0.1805675996
## 6 1.0000000 7.468761e-12 0.999181603 8.183967e-04 0.98879474 0.0112052617
## Class 5: 0 Class5: 1
## 1 1.000000000 4.719597e-12
## 2 0.001056039 9.989440e-01
## 3 0.437419521 5.625805e-01
## 4 0.986386667 1.361333e-02
## 5 0.991598539 8.401461e-03
## 6 0.684410151 3.155898e-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 6.001452e-15 9.906941e-01 0.9550395125 4.719597e-12
## 2 3.181332e-17 7.398858e-07 0.0000391401 9.989440e-01
## 3 6.037856e-13 3.414132e-04 0.0290586887 5.625805e-01
## 4 6.676399e-12 5.630880e-08 0.0011175627 1.361333e-02
## 5 1.098094e-03 9.848452e-02 0.1805675996 8.401461e-03
## 6 7.468761e-12 8.183967e-04 0.0112052617 3.155898e-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 6.001452e-15 9.906941e-01 9.550395e-01 4.719597e-12 3 4
## 2 3.181332e-17 7.398858e-07 3.914010e-05 9.989440e-01 5 5
## 3 6.037856e-13 3.414132e-04 2.905869e-02 5.625805e-01 5 5
## 4 6.676399e-12 5.630880e-08 1.117563e-03 1.361333e-02 5 4
## 5 1.098094e-03 9.848452e-02 1.805676e-01 8.401461e-03 4 4
## 6 7.468761e-12 8.183967e-04 1.120526e-02 3.155898e-01 5 4
## 7 1.486935e-16 4.908099e-14 5.193028e-02 3.163565e-06 4 5
## 8 5.058822e-17 9.418505e-01 9.992426e-01 1.247683e-12 4 3
## 9 2.765434e-15 9.908833e-12 1.989161e-01 8.310777e-01 5 5
## 10 3.778118e-08 3.336396e-04 1.145115e-05 4.246143e-06 3 3
## 11 9.163018e-18 5.229629e-06 9.692590e-01 2.468752e-01 4 4
## 12 1.230354e-08 2.637943e-03 7.409994e-02 3.266187e-03 4 3
## 13 6.080784e-12 8.008646e-02 2.259257e-04 9.490554e-01 5 5
## 14 8.566994e-06 5.082716e-01 4.494616e-01 6.594524e-07 3 4
## 15 2.527135e-08 8.553491e-01 2.777116e-01 5.039626e-04 3 4
## 16 2.832041e-21 1.612568e-07 5.939345e-04 7.502168e-01 5 5
## 17 4.576277e-13 7.192674e-05 8.128018e-02 3.333484e-02 4 4
## 18 1.248735e-08 2.024982e-07 1.898244e-01 9.715559e-01 5 5
## 19 3.173990e-07 3.133991e-01 5.606297e-01 2.479641e-04 4 4
## 20 1.961047e-11 1.393589e-03 3.691807e-01 7.632794e-01 5 2
## 21 1.113461e-19 5.939070e-07 7.468638e-01 6.197906e-05 4 4
## 22 1.657680e-21 3.532695e-05 4.249304e-01 3.954706e-01 4 4
## 23 4.112728e-02 9.970773e-01 2.477833e-03 3.786653e-11 3 3
## 24 7.067206e-18 1.999499e-06 3.439433e-01 9.960929e-01 5 5
## 25 2.760326e-10 1.699007e-01 3.206543e-01 2.507304e-02 4 4
## 26 1.553486e-12 2.886310e-03 1.551287e-01 7.726985e-01 5 5
## 27 1.513393e-11 4.615788e-08 4.406566e-01 7.591389e-01 5 4
## 28 7.005726e-09 7.259370e-05 2.359342e-03 8.832113e-01 5 5
## 29 8.530547e-12 1.240386e-06 1.546888e-01 3.459301e-05 4 3
## 30 1.526236e-07 3.282748e-02 1.168820e-01 5.853398e-15 4 3
## 31 1.338523e-12 5.360152e-07 2.399881e-03 9.997323e-01 5 5
## 32 3.546015e-16 1.202368e-03 2.192342e-04 9.982605e-01 5 5
## 33 4.124607e-15 4.823223e-05 9.176736e-01 8.167380e-01 4 5
## 34 1.190177e-10 5.989509e-01 9.030221e-02 2.755955e-05 3 3
## 35 2.238154e-10 3.784548e-02 7.585264e-01 1.560269e-01 4 4
## 36 4.907692e-13 9.707572e-01 7.715131e-01 1.306057e-03 3 4
## 37 3.887534e-08 8.277742e-06 9.939964e-01 1.541870e-08 4 5
## 38 2.292010e-02 9.982899e-01 9.828701e-01 3.668949e-09 3 3
## 39 6.141069e-16 1.617144e-08 3.682211e-04 9.994800e-01 5 5
## 40 3.383928e-10 3.522757e-01 3.298282e-01 2.596484e-06 3 3
## 41 8.890503e-16 6.747855e-06 7.270403e-04 9.998888e-01 5 5
## 42 1.378122e-16 5.827288e-05 9.091239e-01 1.259431e-03 4 4
## 43 4.650186e-14 6.478019e-12 8.579096e-02 2.698454e-04 4 5
## 44 3.201577e-09 7.908794e-04 9.344346e-01 9.311830e-02 4 3
## 45 3.347056e-10 1.844894e-01 9.019845e-01 3.652591e-02 4 5
## 46 1.294661e-05 3.010479e-02 6.266453e-01 2.443137e-02 4 4
## 47 2.082798e-10 5.431317e-04 8.288393e-01 4.765672e-01 4 5
## 48 1.004841e-09 6.667661e-01 1.198246e-01 7.979276e-02 3 3
## 49 8.603855e-13 9.439554e-06 6.387894e-03 9.999069e-01 5 5
## 50 6.312095e-09 6.157354e-05 1.010949e-02 9.921897e-01 5 5
## 51 1.411014e-12 3.702423e-05 1.327795e-02 9.996248e-01 5 4
## 52 1.425473e-09 9.787957e-05 9.330043e-01 1.800862e-01 4 5
## 53 3.731115e-11 9.412171e-04 9.813055e-03 9.823678e-01 5 4
## 54 4.276763e-12 8.558309e-07 9.533920e-01 6.568320e-01 4 4
## 55 8.673240e-11 6.494330e-02 2.283214e-01 5.031745e-01 5 4
## 56 5.233609e-12 5.770715e-06 1.043967e-03 9.999260e-01 5 5
## 57 2.423391e-10 6.701274e-05 2.712067e-02 9.989880e-01 5 2
## 58 7.333474e-13 1.185030e-06 1.648736e-02 9.996892e-01 5 5
## 59 7.463351e-13 2.670603e-05 1.196349e-02 9.997201e-01 5 4
## 60 2.272586e-09 8.923739e-01 8.288749e-01 2.951601e-03 3 4
## 61 6.295916e-10 9.646038e-05 2.240396e-02 9.986526e-01 5 5
## 62 8.074892e-13 1.261309e-04 5.018615e-04 9.999264e-01 5 5
## 63 3.985992e-07 2.669042e-02 8.050055e-01 2.564465e-01 4 4
## 64 9.311093e-14 5.175921e-05 2.063804e-01 9.916944e-01 5 5
## 65 7.792182e-15 1.032595e-06 3.099897e-03 9.999565e-01 5 5
## 66 7.708184e-13 9.047101e-09 2.314777e-03 9.999889e-01 5 5
## 67 4.328525e-08 1.052521e-05 6.470601e-02 9.473539e-01 5 3
## 68 6.772665e-08 7.117152e-02 1.902835e-01 5.815065e-01 5 3
## 69 9.926787e-12 2.860534e-04 4.203444e-01 8.425308e-01 5 4
## 70 4.379712e-15 3.203519e-06 1.769341e-01 9.649320e-01 5 4
## 71 8.744197e-12 8.954362e-07 6.371563e-03 9.999111e-01 5 5
## 72 3.965537e-13 3.399792e-07 3.624347e-02 9.994160e-01 5 5
## 73 7.520040e-07 1.481108e-02 1.415362e-01 6.300347e-01 5 2
## 74 9.343996e-08 3.730118e-02 1.300895e-01 8.335105e-01 5 4
## 75 4.653090e-12 1.135142e-05 5.029482e-01 9.781002e-01 5 4
## 76 2.100098e-13 1.587153e-06 6.984742e-01 2.541617e-01 4 5
## 77 1.387082e-16 2.130777e-04 7.921604e-05 2.473371e-02 5 3
## 78 6.617421e-19 1.249026e-04 9.028184e-01 3.421292e-01 4 4
## 79 2.707915e-17 1.664069e-03 5.263536e-01 8.006974e-08 4 5
## 80 1.239308e-12 1.326879e-08 1.106199e-04 9.999959e-01 5 5
## 81 4.144759e-16 4.327454e-07 2.663230e-01 9.509143e-01 5 5
## 82 1.467176e-17 1.467771e-04 2.236165e-02 9.934681e-01 5 4
## 83 1.848112e-18 6.493151e-07 9.794285e-05 7.339759e-06 4 5
## 84 2.488353e-11 1.471024e-05 5.646487e-03 9.753977e-01 5 5
## 85 6.275407e-14 3.044181e-03 2.586339e-01 5.177658e-01 5 5
## 86 1.054379e-12 6.697169e-04 2.480797e-03 2.802755e-06 4 3
## 87 1.361106e-04 1.392428e-02 9.978718e-01 4.211468e-07 4 4
## 88 1.330511e-10 8.945459e-04 5.570522e-04 1.531007e-03 5 5
## 89 4.025219e-20 7.643828e-08 1.235188e-02 9.998828e-01 5 5
## 90 1.482383e-12 5.179879e-06 7.938875e-01 2.801919e-01 4 3
## 91 2.071324e-13 5.372523e-02 2.576057e-01 8.091777e-01 5 3
## 92 3.883670e-14 2.012340e-02 4.084491e-01 8.623201e-02 4 5
## 93 1.177079e-21 2.332167e-05 9.986821e-01 3.670294e-04 4 4
## 94 1.058850e-13 7.826680e-15 4.443425e-03 9.889218e-04 4 5
## 95 4.773627e-10 1.531152e-04 6.117664e-05 9.926191e-01 5 5
## 96 1.588680e-22 1.080515e-06 5.052748e-01 9.865903e-01 5 5
## 97 2.143388e-17 2.609169e-05 5.331907e-03 9.992893e-01 5 5
## 98 9.096377e-17 5.283243e-05 2.970764e-03 9.971839e-01 5 5
## 99 5.264715e-01 2.003734e-03 1.357267e-03 5.931403e-02 2 2
## 100 1.158763e-10 2.006690e-03 3.433756e-01 4.096728e-01 5 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] 0.6346154Precision## 2
## 0.6346154Recall## 2
## 0.6260681