SVM v2

PREPARATION

setwd("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/SVM/3.Feature Set 2/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 2.

#Import Features
Features1 <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/SVM/3.Feature Set 2/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/3.Feature Set 2/Combined/Feature Set 1 10th TP.csv")

Features2 <- Features2[-1]

Combine both feature sets into one.

#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))

TRANSFORM FEATURES TO NUMERIC VARIABLES

#Transform Integer to Factor
for(i in 1:1818){
  Features[,i] <- as.numeric(Features[,i])
}
str(Features)

## 'data.frame':    1000 obs. of  1818 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

The probabilities were used as in put for the voting process. The class with the highest probability was picked.

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  0.032606399 0.9673936  0.7655320 0.23446799  0.6325080 0.3674920
## 14 0.007091264 0.9929087  0.9360034 0.06399659  0.6503677 0.3496323
## 16 0.017050214 0.9829498  0.9135978 0.08640221  0.7657297 0.2342703
## 26 0.041796686 0.9582033  0.8787862 0.12121381  0.6842644 0.3157356
## 28 0.036681125 0.9633189  0.8705713 0.12942866  0.6870545 0.3129455
## 29 0.018772442 0.9812276  0.7804926 0.21950738  0.6183516 0.3816484
##    Class 5: 0 Class5: 1
## 5   0.8941866 0.1058134
## 14  0.4802947 0.5197053
## 16  0.4851715 0.5148285
## 26  0.6543982 0.3456018
## 28  0.5365838 0.4634162
## 29  0.8969489 0.1030511

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  0.032606399 0.23446799 0.3674920 0.1058134
## 14 0.007091264 0.06399659 0.3496323 0.5197053
## 16 0.017050214 0.08640221 0.2342703 0.5148285
## 26 0.041796686 0.12121381 0.3157356 0.3456018
## 28 0.036681125 0.12942866 0.3129455 0.4634162
## 29 0.018772442 0.21950738 0.3816484 0.1030511

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   0.032606399 0.23446799 0.3674920 0.10581341    4      4
## 14  0.007091264 0.06399659 0.3496323 0.51970532    5      5
## 16  0.017050214 0.08640221 0.2342703 0.51482848    5      5
## 26  0.041796686 0.12121381 0.3157356 0.34560178    5      4
## 28  0.036681125 0.12942866 0.3129455 0.46341620    5      4
## 29  0.018772442 0.21950738 0.3816484 0.10305112    4      4
## 39  0.209418853 0.08885445 0.2970308 0.13964807    4      5
## 40  0.010244384 0.21574740 0.6224759 0.07995303    4      3
## 60  0.011150967 0.03874132 0.2646629 0.71067584    5      5
## 61  0.071306783 0.23087797 0.2442283 0.29049646    5      3
## 72  0.006851367 0.07578264 0.3376402 0.65799756    5      4
## 81  0.013805339 0.11326370 0.3473421 0.34165389    4      3
## 86  0.023424199 0.11889265 0.2536982 0.62475531    5      5
## 90  0.060900504 0.17288752 0.3330947 0.11758207    4      4
## 92  0.037649010 0.13955962 0.3369531 0.11608050    4      4
## 113 0.050518104 0.09400428 0.3807871 0.17893788    4      5
## 116 0.020793436 0.13847100 0.2918954 0.37548295    5      4
## 117 0.021556028 0.08413253 0.3323288 0.37363888    5      5
## 122 0.048140342 0.12381778 0.3632241 0.09056707    4      4
## 123 0.019112832 0.07344736 0.3367593 0.26968452    4      2
## 124 0.034318613 0.14768061 0.3420099 0.08327561    4      4
## 131 0.005150139 0.14339867 0.2964182 0.57168645    5      4
## 135 0.317934541 0.21195130 0.4892647 0.03715908    4      3
## 137 0.004774791 0.04260445 0.3582131 0.53201838    5      5
## 140 0.021922183 0.15968793 0.3453923 0.25014746    4      4
## 142 0.018291605 0.06747823 0.3367159 0.51868446    5      5
## 149 0.012709303 0.08312987 0.3549414 0.54168429    5      4
## 154 0.029161309 0.13615018 0.2408427 0.37359171    5      5
## 156 0.154185553 0.07466628 0.3646861 0.15724600    4      3
## 158 0.301087943 0.22850925 0.4854586 0.01748909    4      3
## 169 0.009127897 0.07601771 0.2107797 0.74574982    5      5
## 185 0.009209584 0.11372472 0.1823496 0.63949864    5      5
## 187 0.004892435 0.07155300 0.4545886 0.34069936    4      5
## 192 0.026463493 0.18218031 0.5275966 0.02675882    4      3
## 194 0.019766791 0.16658948 0.4273633 0.19904093    4      4
## 195 0.016788580 0.20780337 0.2652114 0.28148210    5      4
## 196 0.109497039 0.26468466 0.4338441 0.02811541    4      5
## 197 0.350999735 0.20662003 0.2476451 0.04735815    2      3
## 199 0.007479748 0.09481312 0.2349246 0.77400284    5      5
## 210 0.113163687 0.16019816 0.4251082 0.02714038    4      3
## 216 0.016621470 0.06202914 0.1522010 0.88517871    5      5
## 220 0.008772767 0.26442969 0.2886023 0.10884332    4      4
## 227 0.168460902 0.03207103 0.4452559 0.14813515    4      5
## 234 0.030111684 0.12578020 0.4211525 0.20042993    4      3
## 240 0.015747215 0.10538289 0.4229518 0.18319728    4      5
## 245 0.056365554 0.10948983 0.3199625 0.36821189    5      4
## 249 0.015271907 0.13530829 0.3084668 0.40699392    5      5
## 261 0.021484076 0.15494783 0.3310668 0.34096100    5      3
## 277 0.010041986 0.07176750 0.2486279 0.90440948    5      5
## 283 0.019249520 0.09110795 0.2703007 0.57871952    5      5
## 290 0.010587572 0.08258827 0.2094098 0.86574474    5      4
## 293 0.014678006 0.07631392 0.3788308 0.20360093    4      5
## 302 0.008774383 0.12969148 0.2868770 0.43735699    5      4
## 305 0.023044639 0.08527211 0.3569291 0.55838739    5      4
## 308 0.024125081 0.13442204 0.2443141 0.41386578    5      4
## 311 0.008560495 0.07587599 0.2256220 0.77867647    5      5
## 320 0.015695883 0.08871228 0.2478818 0.74196040    5      2
## 322 0.022218242 0.06416225 0.2351916 0.86504481    5      5
## 330 0.012155286 0.07566696 0.1976253 0.88612177    5      4
## 332 0.035834409 0.11152124 0.4295768 0.17657429    4      4
## 333 0.024169947 0.08667697 0.2776611 0.74226306    5      5
## 339 0.009990171 0.09540054 0.2708564 0.59144827    5      5
## 341 0.022255404 0.09117063 0.4701358 0.16467591    4      4
## 344 0.037259399 0.06256462 0.3319810 0.75337246    5      5
## 349 0.010742651 0.08276375 0.1845408 0.86617692    5      5
## 355 0.016007428 0.07055416 0.2016546 0.90676788    5      5
## 356 0.027046900 0.08707521 0.2890443 0.53278459    5      3
## 365 0.012429089 0.14164939 0.2796232 0.37061438    5      3
## 366 0.009883874 0.13385599 0.3172141 0.48020466    5      4
## 369 0.007369483 0.09301260 0.3207309 0.40360596    5      4
## 371 0.010020978 0.08764833 0.2171809 0.81649831    5      5
## 373 0.013084398 0.06656946 0.3143456 0.71399267    5      5
## 389 0.043397696 0.10383520 0.2825576 0.48901900    5      2
## 390 0.032925451 0.13503197 0.2669948 0.49302533    5      4
## 396 0.015626937 0.06741926 0.4246627 0.28765802    4      4
## 412 0.004523162 0.07908335 0.3881505 0.36824381    4      5
## 413 0.013814542 0.10853714 0.3723349 0.30215291    4      3
## 415 0.010149141 0.10363443 0.3543091 0.44189820    5      4
## 422 0.043730152 0.13190479 0.3510138 0.29652186    4      5
## 425 0.015674218 0.07297629 0.1976732 0.94095245    5      5
## 434 0.015359654 0.05913168 0.3303814 0.47266726    5      5
## 438 0.005915523 0.10161784 0.3089868 0.70846787    5      4
## 441 0.108542809 0.18001043 0.3359980 0.15573625    4      5
## 442 0.022348608 0.09572089 0.1895699 0.68017157    5      5
## 445 0.011116547 0.12241480 0.3943433 0.47320410    5      5
## 447 0.032681981 0.15880581 0.3608650 0.08255829    4      3
## 453 0.052578982 0.13357695 0.4161222 0.23995448    4      4
## 454 0.170417899 0.06828422 0.2504082 0.13730463    4      5
## 462 0.006115003 0.06146029 0.2142719 0.83139529    5      5
## 474 0.025622707 0.10220542 0.3070538 0.47872700    5      3
## 476 0.015173593 0.18025155 0.2781339 0.30895262    5      3
## 493 0.014359570 0.11379836 0.2442553 0.44591768    5      5
## 502 0.015131971 0.20422809 0.4099599 0.20374181    4      4
## 503 0.133142075 0.03575333 0.3148697 0.24769091    4      5
## 506 0.015324274 0.13829324 0.2657904 0.52110134    5      5
## 508 0.012934800 0.05020800 0.4031394 0.74450817    5      5
## 512 0.023891480 0.12255654 0.1984804 0.71526307    5      5
## 513 0.022664233 0.11023482 0.2032672 0.41891017    5      5
## 521 0.186357310 0.07327935 0.2726400 0.55242065    5      2
## 524 0.026304197 0.14001703 0.3234707 0.29224615    4      5

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