PREPARATION
setwd("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/5.Feature Set 4/Activation")
#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$Score
#Import Features
Features <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/5.Feature Set 4/Activation/Feature Set 3 TF.csv")
Features <- Features[-1]
RECODE LABELS FOR ONE-VS-ALL
#Class 2
Label2 <- list()
for(i in 1:1000){
if(Label[i]==3| Label[i]==4){
Label2[i] <- 1
}else{
Label2[i] <- 0
}
}
#As Factor
Label2 <- as.factor(unlist(Label2))
#Class 3
Label3 <- list()
for(i in 1:1000){
if(Label[i]==5| Label[i]==6){
Label3[i] <- 1
}else{
Label3[i] <- 0
}
}
#As Factor
Label3 <- as.factor(unlist(Label3))
#Class 4
Label4 <- list()
for(i in 1:1000){
if(Label[i]==7| Label[i]==8){
Label4[i] <- 1
}else{
Label4[i] <- 0
}
}
#As Factor
Label4 <- as.factor(unlist(Label4))
#Class 5
Label5 <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
Label5[i] <- 1
}else{
Label5[i] <- 0
}
}
#As Factor
Label5 <- as.factor(unlist(Label5))
#All Labels
All <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
All[i] <- 5
}else if(Label[i]==7| Label[i]==8){
All[i] <- 4
}else if(Label[i]==5| Label[i]==6){
All[i] <- 3
}else{
All[i] <- 2
}
}
#As Factor
All <- as.factor(unlist(All))
#Control
Control.df <- data.frame(matrix(seq(1,1000),ncol=1,nrow=1000))
Control.df$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 1:438){
Features[,i] <- as.factor(Features[,i])
}
str(Features)
## 'data.frame': 1000 obs. of 438 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 0.9999997 2.655613e-07 0.80116318 1.988368e-01 0.20026054 0.7997395
## 2 1.0000000 3.142917e-10 0.99998526 1.473829e-05 0.02857157 0.9714284
## 3 1.0000000 2.054783e-09 0.99999445 5.552507e-06 0.30488885 0.6951111
## 4 1.0000000 8.600777e-12 0.97973772 2.026228e-02 0.88502852 0.1149715
## 5 0.9999940 5.969138e-06 0.84559727 1.544027e-01 0.68709640 0.3129036
## 6 1.0000000 2.062124e-10 0.08867787 9.113221e-01 0.63676986 0.3632301
## Class 5: 0 Class5: 1
## 1 0.9993263 0.0006736839
## 2 0.9273667 0.0726333343
## 3 0.8725631 0.1274369432
## 4 0.9879256 0.0120744378
## 5 0.8874529 0.1125471166
## 6 0.9760986 0.0239013988
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 2.655613e-07 1.988368e-01 0.7997395 0.0006736839
## 2 3.142917e-10 1.473829e-05 0.9714284 0.0726333343
## 3 2.054783e-09 5.552507e-06 0.6951111 0.1274369432
## 4 8.600777e-12 2.026228e-02 0.1149715 0.0120744378
## 5 5.969138e-06 1.544027e-01 0.3129036 0.1125471166
## 6 2.062124e-10 9.113221e-01 0.3632301 0.0239013988
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 2.655613e-07 1.988368e-01 0.7997394561 6.736839e-04 4 4
## 2 3.142917e-10 1.473829e-05 0.9714284338 7.263333e-02 4 5
## 3 2.054783e-09 5.552507e-06 0.6951111484 1.274369e-01 4 5
## 4 8.600777e-12 2.026228e-02 0.1149714778 1.207444e-02 4 4
## 5 5.969138e-06 1.544027e-01 0.3129035982 1.125471e-01 4 4
## 6 2.062124e-10 9.113221e-01 0.3632301394 2.390140e-02 3 4
## 7 5.242644e-18 4.239923e-10 0.0004114254 1.554365e-02 5 5
## 8 2.890720e-18 1.702285e-03 0.9807003686 3.771457e-05 4 3
## 9 6.748937e-09 8.243791e-09 0.3508720527 6.521287e-01 5 5
## 10 9.694766e-01 1.288981e-05 0.0463873189 4.234738e-04 2 3
## 11 1.262753e-09 6.964375e-07 0.6844671791 6.448101e-01 4 4
## 12 5.361126e-06 2.151879e-03 0.1426415199 2.414849e-01 5 3
## 13 2.917912e-06 1.385290e-03 0.0404306767 8.834922e-01 5 5
## 14 4.690301e-05 1.218133e-02 0.4398788250 1.167002e-01 4 4
## 15 4.506699e-05 1.606016e-02 0.6848038847 1.587296e-02 4 4
## 16 1.727633e-09 1.444033e-01 0.1826584910 2.219058e-01 5 5
## 17 5.046957e-10 9.909637e-07 0.9632082581 5.608868e-03 4 4
## 18 2.289142e-07 2.993031e-03 0.1345139374 9.475254e-01 5 5
## 19 1.222385e-03 2.204001e-01 0.6554475008 4.735628e-03 4 4
## 20 2.236144e-10 3.063744e-05 0.8578122196 6.132854e-01 4 2
## 21 2.042171e-07 3.253529e-03 0.0860402366 3.577717e-02 4 4
## 22 3.788981e-13 3.772896e-04 0.0532859608 9.820202e-01 5 4
## 23 5.113685e-06 1.235652e-01 0.9225390536 3.915383e-08 4 3
## 24 5.297759e-10 1.093757e-06 0.3722758913 9.440974e-01 5 5
## 25 4.919206e-05 6.042907e-02 0.4644827068 7.235779e-02 4 4
## 26 1.775409e-07 4.079852e-03 0.1348274602 3.795561e-01 5 5
## 27 2.811133e-08 7.427024e-02 0.1911380392 1.671703e-01 4 4
## 28 1.077465e-04 1.590572e-03 0.0174400887 9.363850e-01 5 5
## 29 9.416446e-10 9.357415e-05 0.3830568617 1.204016e-03 4 3
## 30 6.936117e-01 3.943400e-01 0.4199938164 1.077282e-08 2 3
## 31 1.218267e-07 2.799221e-04 0.0066813354 9.969257e-01 5 5
## 32 8.960512e-13 2.081847e-05 0.0003281326 9.997282e-01 5 5
## 33 7.740632e-14 3.056706e-03 0.3907936866 8.822281e-01 5 5
## 34 6.744143e-09 4.183615e-01 0.0113734693 5.289894e-03 3 3
## 35 2.005250e-05 1.104835e-02 0.5728572327 6.862827e-02 4 4
## 36 1.514255e-06 3.451023e-01 0.5090179626 2.082333e-02 4 4
## 37 2.612860e-04 2.220163e-01 0.8209644586 1.629828e-04 4 5
## 38 4.819287e-01 9.999495e-01 0.3035754436 9.321091e-07 3 3
## 39 7.051474e-15 4.149685e-05 0.0012253344 9.995326e-01 5 5
## 40 6.091679e-10 4.613097e-03 0.3777347741 5.474287e-03 4 3
## 41 1.102717e-11 1.750121e-05 0.0010425630 9.991506e-01 5 5
## 42 3.872861e-15 4.413651e-04 0.9953423800 1.049112e-03 4 4
## 43 7.147706e-17 1.781945e-12 0.0369144848 3.882091e-01 5 5
## 44 2.993710e-06 3.230362e-02 0.4535791425 4.631278e-01 5 3
## 45 5.388949e-07 7.658646e-01 0.8859808267 8.447815e-03 4 5
## 46 1.939317e-03 3.563867e-03 0.2845008949 1.050590e-01 4 4
## 47 2.301877e-06 3.733725e-02 0.2874628968 7.711115e-01 5 5
## 48 1.387764e-08 1.405565e-02 0.4504061788 8.136595e-01 5 3
## 49 3.409071e-08 1.115080e-03 0.0623922820 9.922896e-01 5 5
## 50 1.547018e-05 4.195725e-02 0.1617788375 6.922738e-01 5 5
## 51 3.932339e-09 1.336463e-02 0.0593682394 9.767654e-01 5 4
## 52 6.621180e-07 2.844816e-03 0.6838928613 3.847942e-01 4 5
## 53 5.750180e-10 5.646249e-03 0.0070050604 9.960219e-01 5 4
## 54 2.142622e-07 1.438675e-04 0.0977456082 9.598066e-01 5 4
## 55 1.594238e-06 3.005243e-01 0.2548461987 5.032325e-01 5 4
## 56 2.413602e-07 9.036956e-03 0.0076587445 9.905519e-01 5 5
## 57 6.269325e-06 1.168963e-02 0.1703190970 9.367151e-01 5 2
## 58 4.037796e-08 4.162097e-05 0.1031935180 9.859988e-01 5 5
## 59 6.336532e-09 3.932945e-04 0.0259183383 9.969672e-01 5 4
## 60 1.307053e-04 7.230633e-01 0.7761339564 4.128434e-03 4 4
## 61 1.777760e-06 8.743162e-03 0.2333249837 8.799397e-01 5 5
## 62 6.865325e-07 3.203618e-02 0.1945276344 8.710479e-01 5 5
## 63 1.391626e-03 4.962516e-02 0.8719704949 5.976799e-02 4 4
## 64 1.006811e-07 2.186003e-04 0.1530036911 9.737363e-01 5 5
## 65 8.046990e-10 2.408888e-03 0.0469246755 9.890228e-01 5 5
## 66 6.253188e-07 1.084056e-02 0.2853522937 8.648479e-01 5 5
## 67 3.348578e-06 6.022462e-02 0.2039946711 7.281016e-01 5 3
## 68 1.460601e-05 9.769360e-02 0.1609559786 7.130644e-01 5 3
## 69 1.688553e-07 9.263328e-02 0.2133059182 3.929025e-01 5 4
## 70 8.637110e-13 1.527509e-02 0.7555767689 1.154635e-01 4 4
## 71 4.286925e-07 9.230925e-05 0.0781477265 9.888275e-01 5 5
## 72 3.422761e-08 9.356919e-04 0.2567690829 9.456578e-01 5 5
## 73 1.232725e-04 1.698244e-02 0.1859602160 7.273542e-01 5 2
## 74 2.605039e-05 5.605096e-02 0.1000526619 8.741595e-01 5 4
## 75 3.614861e-06 1.041068e-04 0.9154228019 3.580003e-01 4 4
## 76 1.027084e-14 6.340817e-05 0.8723041528 3.932359e-01 4 5
## 77 1.555951e-11 2.082323e-04 0.0562709922 2.740505e-02 4 3
## 78 2.764345e-12 4.629849e-06 0.9475536152 3.436419e-01 4 4
## 79 1.246868e-10 7.324763e-05 0.0012913072 2.652141e-02 5 5
## 80 6.155525e-08 5.187666e-05 0.0591286135 9.964674e-01 5 5
## 81 4.299373e-11 5.872194e-05 0.2428964000 8.891587e-01 5 5
## 82 4.550948e-11 3.165506e-05 0.4650974276 5.064684e-01 5 4
## 83 1.117334e-12 3.783435e-05 0.0001297522 8.694005e-04 5 5
## 84 1.644268e-07 8.092749e-03 0.0050578235 9.915347e-01 5 5
## 85 1.356917e-09 1.045458e-01 0.3683034323 1.214723e-01 4 5
## 86 2.977316e-08 9.494999e-01 0.7744947074 4.726172e-06 3 3
## 87 2.495838e-07 3.268663e-01 0.2637258054 1.606251e-01 3 4
## 88 2.026730e-09 1.384047e-07 0.0043708239 4.711554e-02 5 5
## 89 9.199006e-16 9.088603e-08 0.0075175983 9.998370e-01 5 5
## 90 5.926665e-14 6.444330e-04 0.2332476109 9.603774e-01 5 3
## 91 8.424190e-12 4.231003e-02 0.0298349389 9.686896e-01 5 3
## 92 1.341098e-08 1.359880e-03 0.3553221928 1.321954e-01 4 5
## 93 1.845370e-16 8.201026e-02 0.9072078792 3.843677e-02 4 4
## 94 6.597295e-10 1.258265e-13 0.0109302914 7.569604e-01 5 5
## 95 4.162784e-07 8.736479e-03 0.5747612464 3.430833e-01 4 5
## 96 2.246189e-15 1.356381e-05 0.7983185515 7.719791e-01 4 5
## 97 5.269457e-10 4.766743e-02 0.0060959181 9.624331e-01 5 5
## 98 1.123174e-07 2.612137e-02 0.0160971664 8.077569e-01 5 5
## 99 2.762272e-01 1.315801e-02 0.0189930348 4.145018e-01 5 2
## 100 6.847615e-09 2.488491e-01 0.4906213928 3.454907e-01 4 5
CM <- table(Evaluation$Actual,Evaluation$Vote)
CM
##
## 2 3 4 5
## 2 0 0 3 4
## 3 2 5 6 12
## 4 0 4 34 24
## 5 0 1 20 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] 0.6346154
Precision
## 2
## 0.6346154
Recall
## 2
## 0.6042048