SVM v2

PREPARATION

setwd("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/SVM/4.Feature Set 3/Combined")

#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 actual labels.

#Import Labels
Labels <- read_excel("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Naive Bayes/1.Labels/Source Data.xlsx")

Label <- Labels$Score

Import Feature Set 3.

#Import Features
Features1 <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/SVM/4.Feature Set 3/Combined/Feature Set 2 TP.csv")

Features1 <- Features1[-1]

Import Feature Set 1.

#Import Features
Features2 <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/SVM/4.Feature Set 3/Combined/Feature Set 1 10th TP.csv")

Features2 <- Features2[-1]

#Import Features
Features <- cbind(Features1, Features2)

RECODE LABELS FOR ONE-VS-ALL

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))

TRANSFORM FEATURES TO NUMERIC VARIABLES

#Transform Integer to Factor
for(i in 1:1879){
  Features[,i] <- as.numeric(Features[,i])
}
str(Features)

## 'data.frame':    1000 obs. of  1879 variables:
##  $ amaz_jj       : num  0 0 0 0 1 0 0 0 0 0 ...
##  $ arriv_jj      : num  1 0 0 0 0 0 0 0 0 0 ...
##  $ bad_jj        : num  0 0 0 1 0 0 0 0 0 0 ...
##  $ basic_jj      : num  0 0 1 0 0 0 0 0 0 0 ...
##  $ beauti_jj     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ befor_jj      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ best_jjs      : num  1 0 0 0 0 0 0 0 0 0 ...
##  $ big_jj        : num  1 0 0 0 0 0 0 0 0 1 ...
##  $ build_jj      : num  0 0 0 1 0 0 0 0 0 0 ...
##  $ central_jj    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ clean_jj      : num  0 0 0 1 0 0 1 0 1 0 ...
##  $ clear_jj      : num  0 0 0 0 1 0 0 0 0 0 ...
##  $ close_jj      : num  1 0 0 0 0 0 0 0 0 0 ...
##  $ cold_jj       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ difficult_jj  : num  0 0 1 0 0 0 0 0 0 0 ...
##  $ due_jj        : num  1 0 0 0 0 0 0 0 0 0 ...
##  $ earl_jj       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ easi_jj       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ english_jj    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ enough_jj     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ excel_jj      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ extra_jj      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ first_jj      : num  0 1 0 0 0 0 0 0 0 0 ...
##  $ free_jj       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ fresh_jj      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ friend_jj     : num  0 0 0 0 0 0 0 1 0 0 ...
##  $ front_jj      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ full_jj       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ general_jj    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ good_jj       : num  0 0 1 0 0 1 0 1 0 1 ...
##  $ great_jj      : num  0 1 0 1 0 1 0 0 0 0 ...
##  $ guest_jjs     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ high_jj       : num  1 0 0 0 0 0 0 1 0 0 ...
##  $ hot_jj        : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ huge_jj       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ littl_jj      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ locat_jj      : num  0 0 0 1 0 0 0 0 0 0 ...
##  $ london_jj     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ loud_jj       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ main_jj       : num  0 0 0 0 0 1 0 0 0 0 ...
##  $ major_jj      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ modern_jj     : num  0 0 0 0 0 1 0 0 0 0 ...
##  $ much_jj       : num  0 0 0 0 0 0 0 0 0 1 ...
##  $ new_jj        : num  1 1 0 0 0 0 0 0 0 0 ...
##  $ next_jj       : num  1 0 0 1 0 0 0 0 0 0 ...
##  $ nice_jj       : num  0 0 1 1 0 0 0 0 0 0 ...
##  $ nois_jj       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ noisi_jj      : num  0 0 0 1 0 0 0 0 0 0 ...
##  $ ok_jj         : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ old_jj        : num  0 0 0 1 0 0 0 0 0 0 ...
##  $ onli_jj       : num  1 0 0 0 0 0 0 0 0 0 ...
##  $ open_jj       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ overal_jj     : num  0 0 0 1 0 0 0 0 0 0 ...
##  $ particular_jj : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ perfect_jj    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ pillow_jj     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ pleasant_jj   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ poor_jj       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ public_jj     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ quiet_jj      : num  0 0 0 0 0 0 1 0 0 0 ...
##  $ realli_jj     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ recept_jj     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ safe_jj       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ second_jj     : num  0 1 0 1 0 0 0 0 0 0 ...
##  $ select_jj     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ servic_jj     : num  0 0 0 0 0 0 0 0 0 1 ...
##  $ short_jj      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ shower_jjr    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ sleep_jj      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ small_jj      : num  1 0 0 0 0 0 0 0 0 0 ...
##  $ spacious_jj   : num  0 0 0 0 0 0 1 0 0 0 ...
##  $ special_jj    : num  1 0 0 0 0 0 0 0 0 0 ...
##  $ standard_jj   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ stay_jj       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ steep_jj      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ super_jj      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ sure_jj       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ underground_jj: num  0 0 0 0 0 0 0 0 0 0 ...
##  $ upgrad_jj     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ veri_jj       : num  0 0 0 0 0 0 0 0 1 0 ...
##  $ warm_jj       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ whole_jj      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ask_vb        : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ bed_vbd       : num  0 0 0 1 0 0 0 0 0 1 ...
##  $ build_vb      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ came_vbd      : num  1 0 0 1 0 0 0 0 0 0 ...
##  $ check_vb      : num  1 0 0 0 0 0 0 0 0 0 ...
##  $ definit_vb    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ done_vbn      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ expens_vbz    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ gave_vbd      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ get_vb        : num  0 0 0 0 1 0 0 0 0 0 ...
##  $ given_vbn     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ go_vb         : num  0 0 1 0 0 0 0 0 0 0 ...
##  $ go_vbp        : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ got_vbd       : num  1 0 0 0 0 0 0 0 0 0 ...
##  $ like_vb       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ love_vb       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ made_vbd      : num  1 0 0 0 0 0 1 0 0 0 ...
##   [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]

SVM MODEL

#SVM2
train2 <- train
train2$Score <- train.labels.2
SVM2 <- svm(Score~.,data = train2,scale = FALSE,probability=TRUE)

train3 <- train
train3$Score <- train.labels.3
SVM3 <- svm(Score~.,data = train3,scale = FALSE,probability=TRUE)

train4 <- train
train4$Score <- train.labels.4
SVM4 <- svm(Score~.,data = train4,scale = FALSE,probability=TRUE)

train5 <- train
train5$Score <- train.labels.5
SVM5 <- svm(Score~.,data = train5,scale = FALSE,probability=TRUE)

P2 <- predict(SVM2,newdata = test,probability = TRUE)
P3 <- predict(SVM3,newdata = test,probability = TRUE)
P4 <- predict(SVM4,newdata = test,probability = TRUE)
P5 <- predict(SVM5,newdata = test,probability = TRUE)

Prob2 <- attr(P2,"probabilities")
Prob3 <- attr(P3,"probabilities")
Prob4 <- attr(P4,"probabilities")
Prob5 <- attr(P5,"probabilities")

VOTING

Use probabilities as an input for voting, choose the class with the highest probability.

Voting.df <- data.frame(Prob2, Prob3,Prob4,Prob5)
colnames(Voting.df) <- c("Class 2: 1","Class2: 0","Class 3: 0","Class3: 1","Class 4: 0","Class4: 1","Class 5: 0","Class5: 1")

head(Voting.df)

##     Class 2: 1 Class2: 0 Class 3: 0  Class3: 1 Class 4: 0 Class4: 1
## 5  0.032550562 0.9674494  0.7673195 0.23268049  0.6310905 0.3689095
## 14 0.007004017 0.9929960  0.9361750 0.06382502  0.6514990 0.3485010
## 16 0.015838913 0.9841611  0.9110876 0.08891237  0.7628006 0.2371994
## 26 0.041522857 0.9584771  0.8796842 0.12031577  0.6792290 0.3207710
## 28 0.035836663 0.9641633  0.8738814 0.12611860  0.6881794 0.3118206
## 29 0.018895675 0.9811043  0.7807466 0.21925345  0.6155611 0.3844389
##    Class 5: 0 Class5: 1
## 5   0.8941061 0.1058939
## 14  0.4793192 0.5206808
## 16  0.4841433 0.5158567
## 26  0.6568386 0.3431614
## 28  0.5358614 0.4641386
## 29  0.8978423 0.1021577

SEQ <- c(1,4,6,8)
Transformed.Voting.df <- Voting.df[SEQ]
colnames(Transformed.Voting.df) <- c("2","3","4","5")
head(Transformed.Voting.df)

##              2          3         4         5
## 5  0.032550562 0.23268049 0.3689095 0.1058939
## 14 0.007004017 0.06382502 0.3485010 0.5206808
## 16 0.015838913 0.08891237 0.2371994 0.5158567
## 26 0.041522857 0.12031577 0.3207710 0.3431614
## 28 0.035836663 0.12611860 0.3118206 0.4641386
## 29 0.018895675 0.21925345 0.3844389 0.1021577

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
## 5   0.032550562 0.23268049 0.3689095 0.10589385    4      4
## 14  0.007004017 0.06382502 0.3485010 0.52068078    5      5
## 16  0.015838913 0.08891237 0.2371994 0.51585673    5      5
## 26  0.041522857 0.12031577 0.3207710 0.34316140    5      4
## 28  0.035836663 0.12611860 0.3118206 0.46413863    5      4
## 29  0.018895675 0.21925345 0.3844389 0.10215766    4      4
## 39  0.246073521 0.08608608 0.3058660 0.13459085    4      5
## 40  0.010332053 0.21082554 0.6178807 0.07994265    4      3
## 60  0.010846452 0.03851740 0.2657178 0.71141036    5      5
## 61  0.071086342 0.23012029 0.2462352 0.29063929    5      3
## 72  0.006675285 0.07550666 0.3373082 0.65823817    5      4
## 81  0.013997219 0.11326034 0.3462024 0.34105034    4      3
## 86  0.024357130 0.11935896 0.2537205 0.62506092    5      5
## 90  0.061167775 0.17215195 0.3327544 0.11786334    4      4
## 92  0.045087920 0.13384530 0.3414363 0.11168607    4      4
## 113 0.052455308 0.09306235 0.3797383 0.17936948    4      5
## 116 0.020254972 0.13633619 0.2912443 0.37570098    5      4
## 117 0.021435263 0.08433783 0.3304387 0.37434007    5      5
## 122 0.048443248 0.12195670 0.3622461 0.09079936    4      4
## 123 0.019235052 0.07308348 0.3401855 0.26781159    4      2
## 124 0.034423440 0.14909550 0.3397862 0.08335382    4      4
## 131 0.005287763 0.14192833 0.2939718 0.57200101    5      4
## 135 0.310196649 0.20521109 0.4921757 0.03699710    4      3
## 137 0.005003248 0.04296714 0.3544315 0.52491196    5      5
## 140 0.021975143 0.15728514 0.3432531 0.25054853    4      4
## 142 0.018187068 0.06725453 0.3359955 0.51905308    5      5
## 149 0.012435937 0.08382654 0.3578403 0.54238464    5      4
## 154 0.028975023 0.13454558 0.2406781 0.37409458    5      5
## 156 0.151300609 0.07594545 0.3650155 0.15751229    4      3
## 158 0.320344826 0.23153542 0.4803940 0.01684115    4      3
## 169 0.008994001 0.07665674 0.2133776 0.74639989    5      5
## 185 0.009239595 0.11469870 0.1826100 0.64002736    5      5
## 187 0.004482111 0.07250963 0.4580091 0.34138676    4      5
## 192 0.026736952 0.18013808 0.5262110 0.02682388    4      3
## 194 0.019218205 0.16590629 0.4260532 0.19942916    4      4
## 195 0.016788508 0.20978710 0.2652889 0.28196475    5      4
## 196 0.110551232 0.26263602 0.4323587 0.02811199    4      5
## 197 0.341871785 0.22087504 0.2419438 0.04635755    2      3
## 199 0.007348658 0.09377933 0.2373930 0.77433902    5      5
## 210 0.114447292 0.16174632 0.4220237 0.02720369    4      3
## 216 0.016529771 0.06460177 0.1527286 0.88519670    5      5
## 220 0.008132184 0.26121812 0.2944043 0.10784683    4      4
## 227 0.173776463 0.03428352 0.4353533 0.14698134    4      5
## 234 0.030108832 0.12581995 0.4201959 0.20096186    4      3
## 240 0.017307318 0.10639089 0.4155558 0.17918504    4      5
## 245 0.056358718 0.10991389 0.3184761 0.36866570    5      4
## 249 0.015402970 0.13646270 0.3089571 0.40749895    5      5
## 261 0.021593676 0.15663638 0.3312962 0.34139224    5      3
## 277 0.009895240 0.07258497 0.2489168 0.90441540    5      5
## 283 0.019227275 0.09150743 0.2717777 0.57921618    5      5
## 290 0.010455049 0.08276155 0.2101070 0.86590398    5      4
## 293 0.014381404 0.07654698 0.3799861 0.20395697    4      5
## 302 0.008748220 0.12921125 0.2864679 0.43780803    5      4
## 305 0.022977748 0.08430635 0.3557554 0.55896227    5      4
## 308 0.023387828 0.13354197 0.2440161 0.41434271    5      4
## 311 0.008339889 0.07577880 0.2266431 0.77913951    5      5
## 320 0.015666404 0.08918620 0.2480184 0.74221259    5      2
## 322 0.022711271 0.06460672 0.2347151 0.86517648    5      5
## 330 0.012062255 0.07604510 0.1985318 0.88623771    5      4
## 332 0.034932718 0.11372799 0.4265310 0.17696722    4      4
## 333 0.024070091 0.08756814 0.2772309 0.74251836    5      5
## 339 0.009854142 0.09496669 0.2713149 0.59207084    5      5
## 341 0.021935800 0.09108508 0.4683688 0.16485910    4      4
## 344 0.037198385 0.06200086 0.3319844 0.75361508    5      5
## 349 0.010315891 0.08307964 0.1864392 0.86631871    5      5
## 355 0.016197906 0.07357972 0.2099958 0.90328258    5      5
## 356 0.027430232 0.08819461 0.2913392 0.53335651    5      3
## 365 0.012276528 0.14160500 0.2806955 0.37108788    5      3
## 366 0.010133360 0.13292298 0.3158287 0.48061224    5      4
## 369 0.007333942 0.09261409 0.3185532 0.40441219    5      4
## 371 0.009884334 0.08769674 0.2200889 0.81661703    5      5
## 373 0.013138648 0.06776095 0.3135537 0.71432581    5      5
## 389 0.043147511 0.10339117 0.2829837 0.48962010    5      2
## 390 0.033023629 0.13395874 0.2669040 0.49354831    5      4
## 396 0.015574651 0.06751296 0.4222426 0.28810329    4      4
## 412 0.004330836 0.07894118 0.3868091 0.36875430    4      5
## 413 0.013076592 0.10680020 0.3736979 0.30230034    4      3
## 415 0.010207170 0.10350553 0.3539418 0.44211351    5      4
## 422 0.040731031 0.12958118 0.3485326 0.29690940    4      5
## 425 0.016013393 0.07345894 0.1980705 0.94095081    5      5
## 434 0.014281470 0.05999869 0.3307957 0.47356061    5      5
## 438 0.005826694 0.10130638 0.3101515 0.70855804    5      4
## 441 0.103954605 0.18089264 0.3186383 0.15943566    4      5
## 442 0.021649950 0.09631189 0.1899239 0.68063516    5      5
## 445 0.010394489 0.12316200 0.3919762 0.47373475    5      5
## 447 0.034515956 0.15466428 0.3540550 0.08178897    4      3
## 453 0.051797745 0.13336283 0.4163475 0.24036597    4      4
## 454 0.162468922 0.06573398 0.2459286 0.13861880    4      5
## 462 0.005875153 0.06203360 0.2149008 0.83141374    5      5
## 474 0.025141850 0.10116051 0.3050142 0.47926309    5      3
## 476 0.014795714 0.18261064 0.2769214 0.30951355    5      3
## 493 0.013956117 0.11586706 0.2432242 0.44631042    5      5
## 502 0.014701484 0.20397582 0.4149309 0.20195727    4      4
## 503 0.139044993 0.03551446 0.3092450 0.24705347    4      5
## 506 0.015032709 0.13912523 0.2661192 0.52205333    5      5
## 508 0.012090469 0.04885160 0.3999136 0.74530293    5      5
## 512 0.023556285 0.12098689 0.1987580 0.71559683    5      5
## 513 0.022104200 0.11021572 0.2044413 0.41942301    5      5
## 521 0.185539093 0.07440826 0.2750607 0.55297875    5      2
## 524 0.025634592 0.13923008 0.3222938 0.29283974    4      5

The confusion matrix was created and accuracy, precision and recall was computed.

CM <- table(Evaluation$Actual,Evaluation$Vote)
CM

##    
##      2  3  4  5
##   2  0  0  3  4
##   3  1  0 15  9
##   4  0  0 27 35
##   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] 0.5769231

Precision

##         2 
## 0.5769231

Recall

##         2 
## 0.4853141