PREPARATION
setwd("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/6.Feature Set 5/Directives")
#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$Score
#Import Features
Features1 <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/5.Feature Set 4/Activation/Feature Set 3 TF.csv")
Features1 <- Features1[-1]
#Import Features
Features2 <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/6.Feature Set 5/Directives/Directives.csv")
Features2 <- Features2[1:1000,]
Features2 <- Features2[4:10]
#Import Features
Features <- cbind(Features1,Features2)
RECODE LABELS FOR ONE-VS-ALL
#Class 2
Label2 <- list()
for(i in 1:1000){
if(Label[i]==3| Label[i]==4){
Label2[i] <- 1
}else{
Label2[i] <- 0
}
}
#As Factor
Label2 <- as.factor(unlist(Label2))
#Class 3
Label3 <- list()
for(i in 1:1000){
if(Label[i]==5| Label[i]==6){
Label3[i] <- 1
}else{
Label3[i] <- 0
}
}
#As Factor
Label3 <- as.factor(unlist(Label3))
#Class 4
Label4 <- list()
for(i in 1:1000){
if(Label[i]==7| Label[i]==8){
Label4[i] <- 1
}else{
Label4[i] <- 0
}
}
#As Factor
Label4 <- as.factor(unlist(Label4))
#Class 5
Label5 <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
Label5[i] <- 1
}else{
Label5[i] <- 0
}
}
#As Factor
Label5 <- as.factor(unlist(Label5))
#All Labels
All <- list()
for(i in 1:1000){
if(Label[i]==9| Label[i]==10){
All[i] <- 5
}else if(Label[i]==7| Label[i]==8){
All[i] <- 4
}else if(Label[i]==5| Label[i]==6){
All[i] <- 3
}else{
All[i] <- 2
}
}
#As Factor
All <- as.factor(unlist(All))
#Control
Control.df <- data.frame(matrix(seq(1,1000),ncol=1,nrow=1000))
Control.df$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 1:445){
Features[,i] <- as.factor(Features[,i])
}
str(Features)
## 'data.frame': 1000 obs. of 445 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 2.560604e-07 0.80071056 1.992894e-01 0.19967292 0.8003271
## 2 1.0000000 3.030474e-10 0.99998522 1.478019e-05 0.02846979 0.9715302
## 3 1.0000000 1.981270e-09 0.99999443 5.568292e-06 0.30411096 0.6958890
## 4 1.0000000 8.293071e-12 0.97968129 2.031871e-02 0.88465424 0.1153458
## 5 0.9999942 5.755584e-06 0.84522626 1.547737e-01 0.68630616 0.3136938
## 6 1.0000000 1.988348e-10 0.08844871 9.115513e-01 0.63591987 0.3640801
## Class 5: 0 Class5: 1
## 1 0.9993260 0.0006739876
## 2 0.9273363 0.0726637215
## 3 0.8725129 0.1274871064
## 4 0.9879202 0.0120798193
## 5 0.8874078 0.1125921750
## 6 0.9760881 0.0239119241
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 2.560604e-07 1.992894e-01 0.8003271 0.0006739876
## 2 3.030474e-10 1.478019e-05 0.9715302 0.0726637215
## 3 1.981270e-09 5.568292e-06 0.6958890 0.1274871064
## 4 8.293071e-12 2.031871e-02 0.1153458 0.0120798193
## 5 5.755584e-06 1.547737e-01 0.3136938 0.1125921750
## 6 1.988348e-10 9.115513e-01 0.3640801 0.0239119241
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 2.560604e-07 1.992894e-01 0.8003270777 6.739876e-04 4 4
## 2 3.030474e-10 1.478019e-05 0.9715302050 7.266372e-02 4 5
## 3 1.981270e-09 5.568292e-06 0.6958890352 1.274871e-01 4 5
## 4 8.293071e-12 2.031871e-02 0.1153457553 1.207982e-02 4 4
## 5 5.755584e-06 1.547737e-01 0.3136938374 1.125922e-01 4 4
## 6 1.988348e-10 9.115513e-01 0.3640801305 2.391192e-02 3 4
## 7 5.055080e-18 4.251977e-10 0.0004129387 1.555055e-02 5 5
## 8 2.787300e-18 1.707116e-03 0.9807697672 3.773159e-05 4 3
## 9 6.507483e-09 8.267228e-09 0.3517090977 6.522310e-01 5 5
## 10 9.683799e-01 1.292646e-05 0.0465500712 4.236648e-04 2 3
## 11 1.217576e-09 6.984174e-07 0.6852599263 6.449134e-01 4 4
## 12 5.169324e-06 2.157984e-03 0.1430913107 2.415676e-01 5 3
## 13 2.813520e-06 1.389223e-03 0.0405734191 8.835386e-01 5 5
## 14 4.522505e-05 1.221554e-02 0.4407840212 1.167467e-01 4 4
## 15 4.345472e-05 1.610509e-02 0.6855961746 1.588000e-02 4 4
## 16 1.665824e-09 1.447544e-01 0.1832075028 2.219837e-01 5 5
## 17 4.866394e-10 9.937809e-07 0.9633382045 5.611384e-03 4 4
## 18 2.207244e-07 3.001515e-03 0.1349421333 9.475478e-01 5 5
## 19 1.178704e-03 2.208883e-01 0.6562765440 4.737754e-03 4 4
## 20 2.156142e-10 3.072454e-05 0.8582596401 6.133924e-01 4 2
## 21 1.969109e-07 3.262748e-03 0.0863295186 3.579274e-02 4 4
## 22 3.653424e-13 3.783618e-04 0.0534715602 9.820281e-01 5 4
## 23 4.930736e-06 1.238730e-01 0.9228011287 3.917149e-08 4 3
## 24 5.108223e-10 1.096866e-06 0.3731346463 9.441212e-01 5 5
## 25 4.743221e-05 6.059045e-02 0.4653964654 7.238807e-02 4 4
## 26 1.711891e-07 4.091404e-03 0.1352564982 3.796623e-01 5 5
## 27 2.710561e-08 7.446566e-02 0.1917065599 1.672331e-01 4 4
## 28 1.038921e-04 1.595087e-03 0.0175031423 9.364119e-01 5 5
## 29 9.079558e-10 9.384015e-05 0.3839252755 1.204559e-03 4 3
## 30 6.858152e-01 3.950182e-01 0.4208888408 1.077768e-08 2 3
## 31 1.174682e-07 2.807176e-04 0.0067057568 9.969270e-01 5 5
## 32 8.639935e-13 2.087766e-05 0.0003293397 9.997283e-01 5 5
## 33 7.463699e-14 3.065370e-03 0.3916685050 8.822750e-01 5 5
## 34 6.502861e-09 4.190525e-01 0.0114148442 5.292268e-03 3 3
## 35 1.933511e-05 1.107941e-02 0.5737557677 6.865711e-02 4 4
## 36 1.460081e-06 3.457442e-01 0.5099359063 2.083253e-02 4 4
## 37 2.519405e-04 2.225070e-01 0.8215037006 1.630563e-04 4 5
## 38 4.728395e-01 9.999497e-01 0.3043525597 9.325296e-07 3 3
## 39 6.799196e-15 4.161481e-05 0.0012298379 9.995328e-01 5 5
## 40 5.873739e-10 4.626151e-03 0.3785985266 5.476743e-03 4 3
## 41 1.063265e-11 1.755096e-05 0.0010463954 9.991510e-01 5 5
## 42 3.734303e-15 4.426193e-04 0.9953593772 1.049584e-03 4 4
## 43 6.891986e-17 1.787011e-12 0.0370452924 3.883162e-01 5 5
## 44 2.886605e-06 3.239248e-02 0.4544896555 4.632399e-01 5 3
## 45 5.196151e-07 7.663733e-01 0.8863513527 8.451594e-03 4 5
## 46 1.870065e-03 3.573963e-03 0.2852491822 1.051014e-01 4 4
## 47 2.219523e-06 3.743943e-02 0.2882158365 7.711911e-01 5 5
## 48 1.338114e-08 1.409505e-02 0.4513155832 8.137279e-01 5 3
## 49 3.287106e-08 1.118246e-03 0.0626075019 9.922930e-01 5 5
## 50 1.491672e-05 4.207152e-02 0.1622775520 6.923699e-01 5 5
## 51 3.791653e-09 1.340212e-02 0.0595736908 9.767757e-01 5 4
## 52 6.384297e-07 2.852881e-03 0.6846863868 3.849010e-01 4 5
## 53 5.544458e-10 5.662210e-03 0.0070306567 9.960237e-01 5 4
## 54 2.065967e-07 1.442764e-04 0.0980700226 9.598240e-01 5 4
## 55 1.537202e-06 3.011214e-01 0.2555443460 5.033452e-01 5 4
## 56 2.327251e-07 9.062414e-03 0.0076867109 9.905562e-01 5 5
## 57 6.045031e-06 1.172248e-02 0.1708387727 9.367419e-01 5 2
## 58 3.893337e-08 4.173929e-05 0.1035339390 9.860051e-01 5 5
## 59 6.109832e-09 3.944122e-04 0.0260112331 9.969686e-01 5 4
## 60 1.260297e-04 7.236314e-01 0.7767715096 4.130289e-03 4 4
## 61 1.714158e-06 8.767800e-03 0.2339826868 8.799873e-01 5 5
## 62 6.619707e-07 3.212433e-02 0.1951038052 8.710985e-01 5 5
## 63 1.341905e-03 4.975922e-02 0.8723799918 5.979334e-02 4 4
## 64 9.707909e-08 2.192216e-04 0.1534803075 9.737478e-01 5 5
## 65 7.759096e-10 2.415720e-03 0.0470892201 9.890277e-01 5 5
## 66 6.029470e-07 1.087104e-02 0.2861019249 8.649006e-01 5 5
## 67 3.228777e-06 6.038550e-02 0.2045917600 7.281909e-01 5 3
## 68 1.408347e-05 9.794413e-02 0.1614526450 7.131566e-01 5 3
## 69 1.628143e-07 9.287218e-02 0.2139229366 3.930101e-01 5 4
## 70 8.328103e-13 1.531785e-02 0.7562544812 1.155095e-01 4 4
## 71 4.133554e-07 9.257166e-05 0.0784127491 9.888325e-01 5 5
## 72 3.300306e-08 9.383495e-04 0.2574706778 9.456810e-01 5 5
## 73 1.188628e-04 1.702990e-02 0.1865168871 7.274437e-01 5 2
## 74 2.511841e-05 5.620136e-02 0.1003838815 8.742091e-01 5 4
## 75 3.485534e-06 1.044027e-04 0.9157067536 3.581040e-01 4 4
## 76 9.903387e-15 6.358842e-05 0.8727127384 3.933435e-01 4 5
## 77 1.500284e-11 2.088242e-04 0.0564663686 2.741707e-02 4 3
## 78 2.665446e-12 4.643011e-06 0.9477358527 3.437437e-01 4 4
## 79 1.202259e-10 7.345586e-05 0.0012960529 2.653306e-02 5 5
## 80 5.935301e-08 5.202414e-05 0.0593332879 9.964690e-01 5 5
## 81 4.145556e-11 5.888887e-05 0.2435725118 8.892031e-01 5 5
## 82 4.388131e-11 3.174505e-05 0.4660113431 5.065811e-01 5 4
## 83 1.077359e-12 3.794190e-05 0.0001302297 8.697924e-04 5 5
## 84 1.585442e-07 8.115570e-03 0.0050763410 9.915384e-01 5 5
## 85 1.308371e-09 1.048119e-01 0.3691584127 1.215205e-01 4 5
## 86 2.870797e-08 9.496358e-01 0.7751355765 4.728304e-06 3 3
## 87 2.406546e-07 3.274912e-01 0.2644396457 1.606859e-01 3 4
## 88 1.954221e-09 1.387981e-07 0.0043868373 4.713579e-02 5 5
## 89 8.869897e-16 9.114441e-08 0.0075450532 9.998371e-01 5 5
## 90 5.714629e-14 6.462638e-04 0.2339051625 9.603946e-01 5 3
## 91 8.122801e-12 4.242521e-02 0.0299414398 9.687033e-01 5 3
## 92 1.293118e-08 1.363741e-03 0.3561640291 1.322472e-01 4 5
## 93 1.779349e-16 8.222424e-02 0.9075166246 3.845345e-02 4 4
## 94 6.361267e-10 1.261842e-13 0.0109700720 7.570434e-01 5 5
## 95 4.013854e-07 8.761099e-03 0.5756587430 3.431849e-01 4 5
## 96 2.165828e-15 1.360237e-05 0.7989092941 7.720585e-01 4 5
## 97 5.080933e-10 4.779647e-02 0.0061182129 9.624494e-01 5 5
## 98 1.082991e-07 2.619368e-02 0.0161554446 8.078269e-01 5 5
## 99 2.690032e-01 1.319493e-02 0.0190615941 4.146113e-01 5 2
## 100 6.602631e-09 2.493802e-01 0.4915393742 3.455927e-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