PREPARATION
setwd("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/3.Feature Set 2/Valence")
#install.packages("naivebayes")
library(naivebayes)
## Warning: package 'naivebayes' was built under R version 3.4.3
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, union
library(psych)
library(ggplot2)
##
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
##
## %+%, alpha
library(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$Score
#Import Features
Features <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/3.Feature Set 2/Valence/Feature Set 2.csv")
Features <- Features[-1]
RECODE LABELS FOR ONE-VS-ALL
#Class 2
Label2 <- list()
for(i in 1:1000){
if(Label[i]==3| Label[i]==4){
Label2[i] <- 1
}else{
Label2[i] <- 0
}
}
#As Factor
Label2 <- as.factor(unlist(Label2))
#Class 3
Label3 <- list()
for(i in 1:1000){
if(Label[i]==5| Label[i]==6){
Label3[i] <- 1
}else{
Label3[i] <- 0
}
}
#As Factor
Label3 <- as.factor(unlist(Label3))
#Class 4
Label4 <- list()
for(i in 1:1000){
if(Label[i]==7| Label[i]==8){
Label4[i] <- 1
}else{
Label4[i] <- 0
}
}
#As Factor
Label4 <- as.factor(unlist(Label4))
#Class 5
Label5 <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
Label5[i] <- 1
}else{
Label5[i] <- 0
}
}
#As Factor
Label5 <- as.factor(unlist(Label5))
#All Labels
All <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
All[i] <- 5
}else if(Label[i]==7| Label[i]==8){
All[i] <- 4
}else if(Label[i]==5| Label[i]==6){
All[i] <- 3
}else{
All[i] <- 2
}
}
#As Factor
All <- as.factor(unlist(All))
#Control
Control.df <- data.frame(matrix(seq(1,1000),ncol=1,nrow=1000))
Control.df$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 FEATURES TO FACTOR VARIABLES
#Transform Integer to Factor
for(i in 1:356){
Features[,i] <- as.factor(Features[,i])
}
str(Features)
## 'data.frame': 1000 obs. of 356 variables:
## $ amaz_jj : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
## $ arriv_jj : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ bad_jj : Factor w/ 3 levels "0","1","2": 1 1 1 2 1 1 1 1 1 1 ...
## $ basic_jj : Factor w/ 3 levels "0","1","2": 1 1 2 1 1 1 1 1 1 1 ...
## $ beauti_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ befor_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ best_jjs : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ big_jj : Factor w/ 3 levels "0","1","2": 3 1 1 1 1 1 1 1 1 3 ...
## $ build_jj : Factor w/ 3 levels "0","1","2": 1 1 1 2 1 1 1 1 1 1 ...
## $ central_jj : Factor w/ 4 levels "0","1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ clean_jj : Factor w/ 5 levels "0","1","2","3",..: 1 1 1 2 1 1 2 1 2 1 ...
## $ clear_jj : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
## $ close_jj : Factor w/ 3 levels "0","1","2": 2 1 1 1 1 1 1 1 1 1 ...
## $ cold_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ difficult_jj : Factor w/ 3 levels "0","1","2": 1 1 2 1 1 1 1 1 1 1 ...
## $ due_jj : Factor w/ 3 levels "0","1","2": 2 1 1 1 1 1 1 1 1 1 ...
## $ earl_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ easi_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ english_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ enough_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ excel_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ extra_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ first_jj : Factor w/ 3 levels "0","1","2": 1 2 1 1 1 1 1 1 1 1 ...
## $ free_jj : Factor w/ 4 levels "0","1","2","5": 1 1 1 1 1 1 1 1 1 1 ...
## $ fresh_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ friend_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 1 ...
## $ front_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ full_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ general_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ good_jj : Factor w/ 6 levels "0","1","2","3",..: 1 1 2 1 1 2 1 3 1 4 ...
## $ great_jj : Factor w/ 6 levels "0","1","2","3",..: 1 4 1 2 1 3 1 1 1 1 ...
## $ guest_jjs : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ high_jj : Factor w/ 3 levels "0","1","2": 3 1 1 1 1 1 1 2 1 1 ...
## $ hot_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ huge_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ littl_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ locat_jj : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ...
## $ london_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ loud_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ main_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...
## $ major_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ modern_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...
## $ much_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 2 ...
## $ new_jj : Factor w/ 3 levels "0","1","2": 2 2 1 1 1 1 1 1 1 1 ...
## $ next_jj : Factor w/ 4 levels "0","1","2","3": 4 1 1 2 1 1 1 1 1 1 ...
## $ nice_jj : Factor w/ 5 levels "0","1","2","3",..: 1 1 2 4 1 1 1 1 1 1 ...
## $ nois_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ noisi_jj : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ...
## $ ok_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ old_jj : Factor w/ 3 levels "0","1","2": 1 1 1 2 1 1 1 1 1 1 ...
## $ onli_jj : Factor w/ 3 levels "0","1","2": 2 1 1 1 1 1 1 1 1 1 ...
## $ open_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ overal_jj : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ...
## $ particular_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ perfect_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ pillow_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ pleasant_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ poor_jj : Factor w/ 3 levels "0","1","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ public_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ quiet_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 2 1 1 1 ...
## $ realli_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ recept_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ safe_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ second_jj : Factor w/ 3 levels "0","1","2": 1 2 1 2 1 1 1 1 1 1 ...
## $ select_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ servic_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 2 ...
## $ short_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ shower_jjr : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ sleep_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ small_jj : Factor w/ 4 levels "0","1","2","3": 2 1 1 1 1 1 1 1 1 1 ...
## $ spacious_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 1 1 1 ...
## $ special_jj : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ standard_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ stay_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ steep_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ super_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ sure_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ underground_jj: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ upgrad_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ veri_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 2 1 ...
## $ warm_jj : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ whole_jj : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ ask_vb : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ bed_vbd : Factor w/ 3 levels "0","1","2": 1 1 1 2 1 1 1 1 1 2 ...
## $ build_vb : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ came_vbd : Factor w/ 3 levels "0","1","2": 2 1 1 3 1 1 1 1 1 1 ...
## $ check_vb : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ definit_vb : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ done_vbn : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ expens_vbz : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ gave_vbd : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ get_vb : Factor w/ 3 levels "0","1","2": 1 1 1 1 3 1 1 1 1 1 ...
## $ given_vbn : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ go_vb : Factor w/ 3 levels "0","1","2": 1 1 2 1 1 1 1 1 1 1 ...
## $ go_vbp : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ got_vbd : Factor w/ 4 levels "0","1","2","3": 3 1 1 1 1 1 1 1 1 1 ...
## $ like_vb : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ love_vb : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ made_vbd : Factor w/ 3 levels "0","1","2": 3 1 1 1 1 1 2 1 1 1 ...
## [list output truncated]
PARTITIONING TRAINING & VALIDATION
#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,]
Labels
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]
NAIVE BAYES MODEL
NB2 <- 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)
PREDICTIONS
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
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 0.9999995 4.650167e-07 0.76428935 2.357107e-01 0.18875253 0.81124747
## 2 1.0000000 5.503472e-10 0.99998169 1.831434e-05 0.02660152 0.97339848
## 3 1.0000000 3.598072e-09 0.99999310 6.899762e-06 0.28954517 0.71045483
## 4 1.0000000 1.971280e-11 0.96478076 3.521924e-02 0.96675015 0.03324985
## 5 0.9999895 1.045234e-05 0.81506149 1.849385e-01 0.67108796 0.32891204
## 6 1.0000000 4.726344e-10 0.05224748 9.477525e-01 0.86879268 0.13120732
## Class 5: 0 Class5: 1
## 1 0.9994594 0.0005406481
## 2 0.9408691 0.0591308893
## 3 0.8951010 0.1048989823
## 4 0.9734803 0.0265196675
## 5 0.9076362 0.0923637585
## 6 0.9482454 0.0517545566
Transformed.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 4.650167e-07 2.357107e-01 0.81124747 0.0005406481
## 2 5.503472e-10 1.831434e-05 0.97339848 0.0591308893
## 3 3.598072e-09 6.899762e-06 0.71045483 0.1048989823
## 4 1.971280e-11 3.521924e-02 0.03324985 0.0265196675
## 5 1.045234e-05 1.849385e-01 0.32891204 0.0923637585
## 6 4.726344e-10 9.477525e-01 0.13120732 0.0517545566
Evaluation <- Transformed.Voting.df
Index <- as.numeric(apply(Transformed.Voting.df,MARGIN = 1,which.max))
Index <- Index+1
Evaluation$Vote <- Index
Evaluation$Actual <- test.labels
head(Evaluation,100)
## 2 3 4 5 Vote Actual
## 1 4.650167e-07 2.357107e-01 8.112475e-01 5.406481e-04 4 4
## 2 5.503472e-10 1.831434e-05 9.733985e-01 5.913089e-02 4 5
## 3 3.598072e-09 6.899762e-06 7.104548e-01 1.048990e-01 4 5
## 4 1.971280e-11 3.521924e-02 3.324985e-02 2.651967e-02 3 4
## 5 1.045234e-05 1.849385e-01 3.289120e-01 9.236376e-02 4 4
## 6 4.726344e-10 9.477525e-01 1.312073e-01 5.175456e-02 3 4
## 7 9.165787e-18 5.589335e-09 6.660558e-06 7.116459e-01 5 5
## 8 5.061857e-18 2.114455e-03 9.820429e-01 3.026308e-05 4 3
## 9 1.181787e-08 1.024407e-08 3.677832e-01 6.006763e-01 5 5
## 10 9.823375e-01 1.601735e-05 4.974777e-02 3.398315e-04 2 3
## 11 2.211170e-09 8.654215e-07 7.001154e-01 5.929509e-01 4 4
## 12 9.387676e-06 2.672617e-03 1.518645e-01 2.034806e-01 5 3
## 13 5.109462e-06 1.720839e-03 4.337924e-02 8.588533e-01 5 5
## 14 8.212762e-05 1.509241e-02 4.580528e-01 9.585252e-02 4 4
## 15 3.883798e-06 1.265436e-01 4.119284e-01 1.538132e-01 4 4
## 16 3.025208e-09 1.733670e-01 1.938838e-01 1.862263e-01 4 5
## 17 8.837582e-10 1.231412e-06 9.657251e-01 4.505650e-03 4 4
## 18 4.008451e-07 3.716564e-03 1.432992e-01 9.354386e-01 5 5
## 19 2.138520e-03 2.599752e-01 6.718449e-01 3.803512e-03 4 4
## 20 1.015542e-08 1.162474e-03 4.982930e-01 7.073658e-01 5 2
## 21 4.680610e-07 5.728591e-03 2.431786e-02 7.638658e-02 5 4
## 22 6.634777e-13 4.687925e-04 5.711624e-02 9.776917e-01 5 4
## 23 1.387754e-04 7.618799e-01 7.233933e-01 4.484394e-08 3 3
## 24 3.194644e-10 7.469452e-07 3.353296e-01 9.662224e-01 5 5
## 25 8.613562e-05 7.400650e-02 4.827970e-01 5.890331e-02 4 4
## 26 3.108868e-07 5.064777e-03 1.436298e-01 3.292546e-01 5 5
## 27 4.922495e-08 9.065749e-02 2.027551e-01 1.387225e-01 4 4
## 28 1.886566e-04 1.975747e-03 1.874473e-02 9.219436e-01 5 5
## 29 1.648887e-09 1.162764e-04 4.005620e-01 9.663543e-04 4 3
## 30 3.164029e-01 2.969502e-01 3.722549e-01 8.652354e-08 4 3
## 31 2.133273e-07 3.478189e-04 7.187032e-03 9.961716e-01 5 5
## 32 1.569050e-12 2.586975e-05 3.531393e-04 9.996612e-01 5 5
## 33 1.355440e-13 3.795573e-03 4.084184e-01 8.573651e-01 5 5
## 34 1.180948e-08 4.719633e-01 1.222993e-02 4.249148e-03 3 3
## 35 3.511284e-05 1.369243e-02 5.907309e-01 5.582554e-02 4 4
## 36 2.651566e-06 3.957036e-01 5.273590e-01 1.677804e-02 4 4
## 37 4.574407e-04 2.617841e-01 8.315097e-01 1.307846e-04 4 5
## 38 7.361952e-01 9.945368e-01 6.131926e-01 5.771123e-06 3 3
## 39 1.234763e-14 5.156515e-05 1.318626e-03 9.994176e-01 5 5
## 40 1.066696e-09 5.726013e-03 3.951525e-01 4.397423e-03 4 3
## 41 1.930936e-11 2.174762e-05 1.121954e-03 9.989417e-01 5 5
## 42 2.669944e-14 2.339388e-03 9.810573e-01 2.014681e-03 4 4
## 43 1.638239e-16 1.904313e-13 7.048503e-02 5.858121e-01 5 5
## 44 5.242188e-06 3.982960e-02 4.718423e-01 4.090527e-01 4 3
## 45 3.249634e-07 6.907704e-01 8.685978e-01 1.422557e-02 4 5
## 46 3.390944e-03 4.424780e-03 2.996900e-01 8.608821e-02 4 4
## 47 4.030743e-06 4.598023e-02 3.027433e-01 7.299705e-01 5 5
## 48 2.430074e-08 1.740676e-02 4.686511e-01 7.779640e-01 5 3
## 49 5.969527e-08 1.385269e-03 6.683092e-02 9.904092e-01 5 5
## 50 2.708908e-05 5.161235e-02 1.719910e-01 6.435133e-01 5 5
## 51 6.885806e-09 1.655375e-02 6.360634e-02 9.712090e-01 5 4
## 52 1.159415e-06 3.532646e-03 6.995571e-01 3.341724e-01 4 5
## 53 1.006898e-09 7.006660e-03 7.535073e-03 9.950473e-01 5 4
## 54 3.751884e-07 1.787694e-04 1.044192e-01 9.504005e-01 5 4
## 55 2.791622e-06 3.480632e-01 2.690474e-01 4.483856e-01 5 4
## 56 4.226389e-07 1.120512e-02 8.237805e-03 9.882528e-01 5 5
## 57 1.097798e-05 1.448493e-02 1.809539e-01 9.223424e-01 5 2
## 58 7.070469e-08 5.171939e-05 1.101937e-01 9.826112e-01 5 5
## 59 1.109572e-08 4.886772e-04 2.783924e-02 9.962232e-01 5 4
## 60 2.288519e-04 7.643984e-01 7.886400e-01 3.315435e-03 4 4
## 61 3.112981e-06 1.084161e-02 2.467240e-01 8.546731e-01 5 5
## 62 1.202167e-06 3.950239e-02 2.062982e-01 8.442413e-01 5 5
## 63 2.434293e-03 6.093255e-02 8.799508e-01 4.853203e-02 4 4
## 64 1.762998e-07 2.716272e-04 1.627694e-01 9.674796e-01 5 5
## 65 1.409085e-09 2.991633e-03 5.032200e-02 9.863567e-01 5 5
## 66 8.655882e-06 8.296279e-03 8.772051e-02 9.687601e-01 5 5
## 67 5.863584e-06 7.375972e-02 2.161843e-01 6.824138e-01 5 3
## 68 2.557589e-05 1.185870e-01 1.711268e-01 6.660088e-01 5 3
## 69 2.956777e-07 1.125795e-01 2.258941e-01 3.418067e-01 5 4
## 70 1.512420e-12 1.891136e-02 7.688892e-01 9.481299e-02 4 4
## 71 7.506710e-07 1.147047e-04 8.360728e-02 9.861147e-01 5 5
## 72 5.993498e-08 1.162465e-03 2.710385e-01 9.331712e-01 5 5
## 73 2.158390e-04 2.101647e-02 1.973394e-01 6.815957e-01 5 2
## 74 4.561519e-05 6.871665e-02 1.068651e-01 8.478867e-01 5 4
## 75 1.659187e-05 3.679464e-04 8.404491e-01 3.483050e-01 4 4
## 76 1.798498e-14 7.879236e-05 8.802665e-01 3.421211e-01 4 5
## 77 2.724581e-11 2.587449e-04 6.030217e-02 2.211002e-02 4 3
## 78 4.840566e-12 5.753232e-06 9.510870e-01 2.958306e-01 4 4
## 79 2.183355e-10 9.101888e-05 1.389614e-03 2.139336e-02 5 5
## 80 1.077876e-07 6.446324e-05 6.335076e-02 9.956014e-01 5 5
## 81 7.528509e-11 7.296923e-05 2.566611e-01 8.655358e-01 5 5
## 82 7.969035e-11 3.933557e-05 4.834141e-01 4.515895e-01 4 4
## 83 1.346074e-11 3.809361e-04 2.539605e-04 2.070016e-04 3 5
## 84 2.879231e-07 1.003667e-02 5.441313e-03 9.894722e-01 5 5
## 85 2.376058e-09 1.266989e-01 3.855557e-01 9.986852e-02 4 5
## 86 1.355508e-09 9.707496e-01 8.717077e-01 1.053429e-05 3 3
## 87 4.370391e-07 3.763302e-01 2.782371e-01 1.331130e-01 3 4
## 88 6.080133e-09 3.470171e-07 1.571435e-02 7.282611e-03 4 5
## 89 1.610812e-15 1.129387e-07 8.086074e-03 9.997969e-01 5 5
## 90 1.037801e-13 8.006734e-04 2.466437e-01 9.510981e-01 5 3
## 91 1.475136e-11 5.204189e-02 3.203656e-02 9.612785e-01 5 3
## 92 2.348359e-08 1.689285e-03 3.723248e-01 1.089209e-01 4 5
## 93 4.229548e-16 1.362107e-01 7.213274e-01 8.180886e-02 4 4
## 94 3.931423e-11 4.448283e-13 4.196839e-02 7.490669e-01 5 5
## 95 7.289331e-07 1.083334e-02 5.926119e-01 2.953147e-01 4 5
## 96 3.933237e-15 1.685489e-05 8.098888e-01 7.309395e-01 4 5
## 97 9.227195e-10 5.855623e-02 6.557598e-03 9.536119e-01 5 5
## 98 1.966758e-07 3.225504e-02 1.730311e-02 7.712486e-01 5 5
## 99 4.005856e-01 1.629865e-02 2.041143e-02 3.622732e-01 2 2
## 100 1.199066e-08 2.916217e-01 5.089867e-01 2.975388e-01 4 5
CM <- table(Evaluation$Actual,Evaluation$Vote)
CM
##
## 2 3 4 5
## 2 1 0 2 4
## 3 1 5 8 11
## 4 0 5 32 25
## 5 0 3 21 90
#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] 0.6153846
Precision
## 2
## 0.6153846
Recall
## 2
## 0.5938968