SVM v2

PREPARATION

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

#Import Features
Features1 <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/SVM/5.Feature Set 4/Activation/Feature Set 4 TP.csv")

Features1 <- Features1[-1]

Import Feature Set F1.

#Import Features
Features2 <- read.csv("~/Google Drive/UM/Smart Services/Thesis/Thesis/Code/SVM/5.Feature Set 4/Combined/Feature Set 1 10th TP.csv")

Features2 <- Features2[-1]

Combine the two feature sets into one.

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

RECODE LABELS FOR ONE-VS-ALL

Recode Labels for One-vs-all classification.

#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 FACTOR VARIABLES

Transform Features to numeric.

#Transform Integer to Factor
for(i in 1:1900){
  Features[,i] <- as.numeric(Features[,i])
}
str(Features)

## 'data.frame':    1000 obs. of  1900 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 SVM probability outputs for the voting process. The one 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  0.033303970 0.9666960  0.7678272 0.23217284  0.6291194 0.3708806
## 14 0.007056059 0.9929439  0.9359528 0.06404716  0.6506787 0.3493213
## 16 0.016223951 0.9837760  0.9117140 0.08828601  0.7639694 0.2360306
## 26 0.041736180 0.9582638  0.8808376 0.11916242  0.6792852 0.3207148
## 28 0.036936294 0.9630637  0.8738221 0.12617789  0.6868954 0.3131046
## 29 0.018599871 0.9814001  0.7810517 0.21894827  0.6160649 0.3839351
##    Class 5: 0 Class5: 1
## 5   0.8939539 0.1060461
## 14  0.4791655 0.5208345
## 16  0.4847509 0.5152491
## 26  0.6561744 0.3438256
## 28  0.5355801 0.4644199
## 29  0.8978492 0.1021508

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  0.033303970 0.23217284 0.3708806 0.1060461
## 14 0.007056059 0.06404716 0.3493213 0.5208345
## 16 0.016223951 0.08828601 0.2360306 0.5152491
## 26 0.041736180 0.11916242 0.3207148 0.3438256
## 28 0.036936294 0.12617789 0.3131046 0.4644199
## 29 0.018599871 0.21894827 0.3839351 0.1021508

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   0.033303970 0.23217284 0.3708806 0.10604608    4      4
## 14  0.007056059 0.06404716 0.3493213 0.52083452    5      5
## 16  0.016223951 0.08828601 0.2360306 0.51524911    5      5
## 26  0.041736180 0.11916242 0.3207148 0.34382560    5      4
## 28  0.036936294 0.12617789 0.3131046 0.46441995    5      4
## 29  0.018599871 0.21894827 0.3839351 0.10215076    4      4
## 39  0.246772675 0.08929656 0.3175293 0.12999570    4      5
## 40  0.010284179 0.20931367 0.6169875 0.07990964    4      3
## 60  0.011293804 0.03966390 0.2641735 0.71153345    5      5
## 61  0.070508384 0.23053184 0.2469306 0.29094113    5      3
## 72  0.006869195 0.07564055 0.3382351 0.65814909    5      4
## 81  0.014440642 0.11395942 0.3466415 0.34128956    4      3
## 86  0.024278529 0.11677592 0.2545300 0.62505310    5      5
## 90  0.061205562 0.17086142 0.3336061 0.11789225    4      4
## 92  0.044456141 0.13408652 0.3418860 0.11168747    4      4
## 113 0.051235090 0.09378322 0.3792471 0.17971553    4      5
## 116 0.020018436 0.13642157 0.2920870 0.37558391    5      4
## 117 0.021560304 0.08428785 0.3304238 0.37433500    5      5
## 122 0.047787615 0.12152986 0.3631324 0.09079826    4      4
## 123 0.019410440 0.07269631 0.3359405 0.27026687    4      2
## 124 0.036128032 0.14892432 0.3430827 0.08243053    4      4
## 131 0.005344224 0.14262620 0.2946443 0.57200179    5      4
## 135 0.303304256 0.20915744 0.5000000 0.03700228    4      3
## 137 0.005119366 0.04300394 0.3556076 0.52435489    5      5
## 140 0.021500725 0.15802947 0.3424626 0.25003341    4      4
## 142 0.018277110 0.06672855 0.3370727 0.51873808    5      5
## 149 0.012509904 0.08352698 0.3580081 0.54182587    5      4
## 154 0.028418806 0.13407878 0.2411333 0.37416727    5      5
## 156 0.151594779 0.07626301 0.3644473 0.15716889    4      3
## 158 0.318966462 0.23310529 0.4791090 0.01684031    4      3
## 169 0.009143826 0.07594445 0.2138236 0.74626001    5      5
## 185 0.009462651 0.11441788 0.1835055 0.63989840    5      5
## 187 0.004525709 0.07257806 0.4561520 0.34122647    4      5
## 192 0.027089342 0.18285736 0.5294428 0.02687675    4      3
## 194 0.019332552 0.16642732 0.4252142 0.19936198    4      4
## 195 0.016870698 0.20760314 0.2653402 0.28195155    5      4
## 196 0.109546872 0.25963743 0.4318205 0.02810534    4      5
## 197 0.342037021 0.22310879 0.2431054 0.04632425    2      3
## 199 0.007668334 0.09434279 0.2353946 0.77421403    5      5
## 210 0.111605332 0.16100451 0.4238772 0.02719948    4      3
## 216 0.016807684 0.06521225 0.1519390 0.88524088    5      5
## 220 0.008330897 0.26367439 0.2932567 0.10778163    4      4
## 227 0.171267895 0.03456918 0.4358244 0.14745261    4      5
## 234 0.029777083 0.12558916 0.4216215 0.20109317    4      3
## 240 0.017580516 0.10584067 0.4163363 0.17888758    4      5
## 245 0.056453116 0.10971994 0.3182563 0.36891169    5      4
## 249 0.015588607 0.13497020 0.3097638 0.40710592    5      5
## 261 0.021711138 0.15409078 0.3312823 0.34091160    5      3
## 277 0.010193221 0.07281647 0.2489131 0.90434043    5      5
## 283 0.019053887 0.09118559 0.2725415 0.57877342    5      5
## 290 0.010500425 0.08233699 0.2103187 0.86580735    5      4
## 293 0.014041408 0.07524279 0.3798596 0.20388760    4      5
## 302 0.008813749 0.12858435 0.2875865 0.43712042    5      4
## 305 0.022969458 0.08460165 0.3560554 0.55904007    5      4
## 308 0.023303759 0.13354464 0.2438321 0.41432766    5      4
## 311 0.008442882 0.07593217 0.2265492 0.77905424    5      5
## 320 0.015594499 0.08865837 0.2484204 0.74203716    5      2
## 322 0.022651478 0.06524167 0.2340957 0.86493843    5      5
## 330 0.012163573 0.07537929 0.1984262 0.88608563    5      4
## 332 0.035214879 0.11410895 0.4286860 0.17701321    4      4
## 333 0.024231482 0.08701141 0.2780419 0.74247966    5      5
## 339 0.009968690 0.09447982 0.2719715 0.59184683    5      5
## 341 0.021750346 0.09115225 0.4694679 0.16483096    4      4
## 344 0.037733571 0.06152891 0.3312103 0.75297361    5      5
## 349 0.010487603 0.08314255 0.1866315 0.86622489    5      5
## 355 0.016253759 0.07299904 0.2096282 0.90318598    5      5
## 356 0.027228639 0.08750635 0.2920757 0.53318682    5      3
## 365 0.012371063 0.14226021 0.2811142 0.37101924    5      3
## 366 0.010252779 0.13244882 0.3151274 0.48038503    5      4
## 369 0.007229544 0.09211705 0.3186334 0.40434077    5      4
## 371 0.009954189 0.08711292 0.2198240 0.81632350    5      5
## 373 0.013013282 0.06740343 0.3120345 0.71383635    5      5
## 389 0.043210579 0.10298358 0.2831975 0.48958709    5      2
## 390 0.032920504 0.13391631 0.2671304 0.49352948    5      4
## 396 0.015556596 0.06755564 0.4263393 0.28721463    4      4
## 412 0.004237077 0.07989220 0.3879760 0.36880881    4      5
## 413 0.013447788 0.10720924 0.3736098 0.30245222    4      3
## 415 0.010087598 0.10473338 0.3536096 0.44193468    5      4
## 422 0.041065789 0.12977479 0.3496221 0.29693751    4      5
## 425 0.015759232 0.07238139 0.1981913 0.94085169    5      5
## 434 0.014414044 0.05949506 0.3290170 0.47330075    5      5
## 438 0.005974022 0.10382974 0.3086364 0.70888421    5      4
## 441 0.109984478 0.18328250 0.3232775 0.15768629    4      5
## 442 0.021904410 0.09576812 0.1904356 0.68052597    5      5
## 445 0.010645691 0.12209476 0.3918464 0.47363459    5      5
## 447 0.034292035 0.15471133 0.3557221 0.08171857    4      3
## 453 0.051609290 0.13413872 0.4176147 0.24029314    4      4
## 454 0.161494094 0.06524101 0.2443413 0.13834204    4      5
## 462 0.006108894 0.06231910 0.2155989 0.83161500    5      5
## 474 0.025483523 0.10227964 0.3064073 0.47941197    5      3
## 476 0.014839942 0.18131808 0.2778684 0.30903863    5      3
## 493 0.014281458 0.11678667 0.2435989 0.44604017    5      5
## 502 0.014547703 0.20557414 0.4139538 0.20225793    4      4
## 503 0.142650846 0.03601647 0.3105615 0.24761164    4      5
## 506 0.014957099 0.13811707 0.2670785 0.52192471    5      5
## 508 0.012197548 0.04959110 0.4006871 0.74546054    5      5
## 512 0.023532462 0.12133994 0.1979834 0.71579769    5      5
## 513 0.022313378 0.11059552 0.2033728 0.41931859    5      5
## 521 0.184671681 0.07430778 0.2750057 0.55265447    5      2
## 524 0.025315749 0.13809010 0.3233416 0.29302854    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