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library(xgboost)
library(caret)
library(e1071) setwd("C:/Users/cmhon/Downloads")ê²½ê³ : The working directory was changed to C:/Users/cmhon/Downloads inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.library(tigerstats )
library(tidyverse)
library(DT)
library(psych)
# PLS:SEM
df =read.csv("df.csv")
colnames(df) [1] "Type" "ID" "X3_card" "X5_pay.amount"
[5] "gender" "age" "region" "smartphone"
[9] "online_perception" "on_Simple" "On_Card" "Off_Simple"
[13] "Off_Card" "WTP" "bag" "shoes"
[17] "tshirt" "clock" "convenience" "pain"
[21] "adoption" "form" df$ID = as.character(df$ID)
df$gender = as.factor(df$gender)
df$age = as.factor(df$age)
df$region = as.factor(df$region)
df$smartphone = as.factor(df$smartphone)
#df$On_Simple = as.numeric(df$On_Simple)
df$On_Card = as.numeric(df$On_Card )
df$Off_Simple = as.numeric(df$Off_Simple)
df$Off_Card = as.numeric(df$Off_Card)#data <- iris # reads the dataset
data = df
head(data) # head() returns the top 6 rows of the dataframe
summary(data) # returns the statistical summary of the data columns Type ID X3_card X5_pay.amount gender
Length:2964 Length:2964 Length:2964 Min. : 10000 F:1444
Class :character Class :character Class :character 1st Qu.: 300000 M:1520
Mode :character Mode :character Mode :character Median : 500000
Mean : 1068121
3rd Qu.: 1000000
Max. :10000000
age region smartphone online_perception on_Simple
20s:460 metropolitan :1704 Apple : 936 Length:2964 Min. : 0
30s:588 non-metropolitan:1260 other : 72 Class :character 1st Qu.: 37500
40s:632 Samsung:1956 Mode :character Median : 125000
50s:712 Mean : 285043
60s:572 3rd Qu.: 300000
Max. :5000000
On_Card Off_Simple Off_Card WTP
Min. : 0 Min. : 0 Min. : 0 Min. : 0
1st Qu.: 0 1st Qu.: 0 1st Qu.: 50000 1st Qu.: 18975
Median : 75000 Median : 30000 Median : 150000 Median : 28000
Mean : 265372 Mean : 183301 Mean : 332731 Mean : 29844
3rd Qu.: 212121 3rd Qu.: 113861 3rd Qu.: 325000 3rd Qu.: 36250
Max. :10000000 Max. :2500000 Max. :6300000 Max. :175775
bag shoes tshirt clock convenience
Min. : -1500 Min. : -5000 Min. : 0 Min. : 0 Min. :1.000
1st Qu.: 21000 1st Qu.: 26000 1st Qu.: 7900 1st Qu.: 10000 1st Qu.:4.000
Median : 35500 Median : 39000 Median : 11000 Median : 16000 Median :6.000
Mean : 41529 Mean : 44322 Mean : 14019 Mean : 19507 Mean :5.237
3rd Qu.: 51000 3rd Qu.: 54000 3rd Qu.: 17800 3rd Qu.: 22500 3rd Qu.:7.000
Max. :199500 Max. :296000 Max. :201800 Max. :501000 Max. :7.000
pain adoption form
Min. :1.000 Min. :1.000 Min. :1.00
1st Qu.:3.000 1st Qu.:3.000 1st Qu.:1.75
Median :5.000 Median :5.000 Median :2.50
Mean :4.832 Mean :4.659 Mean :2.50
3rd Qu.:6.000 3rd Qu.:7.000 3rd Qu.:3.25
Max. :7.000 Max. :7.000 Max. :4.00 dim(data)[1] 2964 22data = na.omit(data)
str(data)'data.frame': 2964 obs. of 22 variables:
$ Type : chr "A" "A" "A" "A" ...
$ ID : chr "1" "1" "1" "1" ...
$ X3_card : chr "YES" "YES" "YES" "YES" ...
$ X5_pay.amount : int 300000 300000 300000 300000 300000 300000 300000 100000 100000 100000 ...
$ gender : Factor w/ 2 levels "F","M": 2 2 2 2 2 2 2 1 1 1 ...
$ age : Factor w/ 5 levels "20s","30s","40s",..: 3 3 3 3 3 3 3 1 1 1 ...
$ region : Factor w/ 2 levels "metropolitan",..: 2 2 2 2 2 2 2 2 2 2 ...
$ smartphone : Factor w/ 3 levels "Apple","other",..: 1 1 1 1 1 1 1 1 1 1 ...
$ online_perception: chr "offline is 30% more expensive " "offline is 30% more expensive " "offline is 30% more expensive " "offline is 30% more expensive " ...
$ on_Simple : num 150000 150000 150000 150000 150000 150000 150000 90000 90000 90000 ...
$ On_Card : num 0 0 0 0 0 0 0 0 0 0 ...
$ Off_Simple : num 0 0 0 0 0 0 0 0 0 0 ...
$ Off_Card : num 150000 150000 150000 150000 150000 150000 150000 10000 10000 10000 ...
$ WTP : num 35000 35000 34750 34750 35250 ...
$ bag : num 34000 34000 33000 34000 33000 32000 34000 14000 12000 19000 ...
$ shoes : num 73000 72000 73000 72000 73000 73000 72000 18000 16000 16000 ...
$ tshirt : num 2000 4000 3000 3000 4000 3000 4000 6000 8000 4000 ...
$ clock : num 31000 30000 30000 30000 31000 29000 30000 8000 9000 9000 ...
$ convenience : int 6 6 6 6 6 6 6 6 6 6 ...
$ pain : int 6 6 6 6 6 6 6 6 6 6 ...
$ adoption : int 6 6 6 6 6 6 6 7 7 7 ...
$ form : int 1 1 1 1 1 1 1 1 1 1 ...data <- data%>%
mutate(htp = on_Simple / X5_pay.amount
)
quantile(data$htp, probs = seq(.1, .9, by = .1)) 10% 20% 30% 40% 50% 60% 70% 80%
0.0000000 0.1000000 0.1274510 0.2500000 0.3000000 0.3000000 0.4545455 0.5000000
90%
0.5600000 data <- data %>%
mutate( htp_y = case_when(
htp <= .1 ~ 0.1,
htp > .1 & htp <= .3 ~ 0.3,
htp > .3 & htp <= .5 ~ 0.5,
htp > .5 ~ 0.7,
TRUE ~ 0
))
# createDataPartition() function from the caret package to split the original dataset into a training and testing set and split data into training (80%) and testing set (20%)
parts = createDataPartition(data$X3_card, p = 0.7, list = F)
train = data[parts, ]
test = data[-parts, ]
X_train = data.matrix(train[,c(5:8, 19:22)]) # independent variables for train
y_train = train[,23] # dependent variables for train
X_test = data.matrix(test[,c(5:8, 19:22)]) # independent variables for test
y_test = test[,23] # dependent variables for test
# convert the train and test data into xgboost matrix type.
xgboost_train = xgb.DMatrix(data=X_train, label=y_train)
xgboost_test = xgb.DMatrix(data=X_test, label=y_test)
#Step 4 - Create a xgboost model
# train a model using our training data
model <- xgboost(data = xgboost_train, # the data
max.depth=3, # max depth
nrounds=50) # max number of boosting iterations[1] train-rmse:0.250295
[2] train-rmse:0.224256
[3] train-rmse:0.209770
[4] train-rmse:0.201843
[5] train-rmse:0.194773
[6] train-rmse:0.190535
[7] train-rmse:0.187136
[8] train-rmse:0.185081
[9] train-rmse:0.183017
[10] train-rmse:0.181942
[11] train-rmse:0.179818
[12] train-rmse:0.178274
[13] train-rmse:0.177495
[14] train-rmse:0.176663
[15] train-rmse:0.175846
[16] train-rmse:0.175016
[17] train-rmse:0.173712
[18] train-rmse:0.172527
[19] train-rmse:0.170960
[20] train-rmse:0.169812
[21] train-rmse:0.169115
[22] train-rmse:0.168392
[23] train-rmse:0.167760
[24] train-rmse:0.167116
[25] train-rmse:0.166189
[26] train-rmse:0.165830
[27] train-rmse:0.165181
[28] train-rmse:0.164877
[29] train-rmse:0.164485
[30] train-rmse:0.163625
[31] train-rmse:0.162820
[32] train-rmse:0.162391
[33] train-rmse:0.162145
[34] train-rmse:0.161212
[35] train-rmse:0.160740
[36] train-rmse:0.159992
[37] train-rmse:0.159201
[38] train-rmse:0.158547
[39] train-rmse:0.158055
[40] train-rmse:0.157647
[41] train-rmse:0.157385
[42] train-rmse:0.157015
[43] train-rmse:0.156774
[44] train-rmse:0.156581
[45] train-rmse:0.155936
[46] train-rmse:0.154990
[47] train-rmse:0.154560
[48] train-rmse:0.154190
[49] train-rmse:0.153761
[50] train-rmse:0.153397 summary(model) Length Class Mode
handle 1 xgb.Booster.handle externalptr
raw 62329 -none- raw
niter 1 -none- numeric
evaluation_log 2 data.table list
call 14 -none- call
params 2 -none- list
callbacks 2 -none- list
feature_names 8 -none- character
nfeatures 1 -none- numeric #use model to make predictions on test data
pred_test = predict(model, xgboost_test)
pred_test [1] 0.49562582 0.34232670 0.30760074 0.30760074 0.47551623 0.64210802 0.64210802
[8] 0.64210802 0.20175675 0.64210802 0.64210802 0.64210802 0.21589974 0.35249218
[15] 0.20475788 0.38703600 0.37628239 0.28732610 0.19198801 0.19784895 0.45655271
[22] 0.30760074 0.41801429 0.20687917 0.31498781 0.51050818 0.46573764 0.10759953
[29] 0.46573764 0.42691743 0.42691743 0.42691743 0.43340710 0.43340710 0.34736627
[36] 0.34394568 0.34394568 0.34394568 0.34394568 0.14561523 0.14561523 0.45529976
[43] 0.45529976 0.39527428 0.46537521 0.50237471 0.20411994 0.35562193 0.35562193
[50] 0.35562193 0.35562193 0.35562193 0.35562193 0.40162998 0.40188736 0.33581066
[57] 0.33581066 0.43571210 0.43571210 0.43571210 0.43571210 0.31092012 0.31092012
[64] 0.15577741 0.44665274 0.51394439 0.23632564 0.23632564 0.23632564 0.23632564
[71] 0.44812590 0.41410708 0.25150108 0.25150108 0.25150108 0.44270265 0.35592285
[78] 0.35592285 0.60760623 0.61399084 0.61399084 0.61399084 0.61399084 0.33988720
[85] 0.32242849 0.45775384 0.35592285 0.30938613 0.41446561 0.26407430 0.13577156
[92] 0.42507175 0.36516449 0.36516449 0.30760074 0.30760074 0.30760074 0.30760074
[99] 0.14157058 0.14157058 0.14157058 0.14157058 0.18862273 0.18862273 0.14846650
[106] 0.14846650 0.14846650 0.18862273 0.18862273 0.18862273 0.18862273 0.38395065
[113] 0.23266022 0.32702810 0.32702810 0.32386610 0.21589974 0.21589974 0.21589974
[120] 0.28585944 0.11885192 0.11885192 0.11885192 0.09480507 0.09480507 0.46779019
[127] 0.31134245 0.45726413 0.45726413 0.05516073 0.15355137 0.16073765 0.16073765
[134] 0.29464060 0.27253437 0.27253437 0.27253437 0.29096037 0.36851513 0.03805347
[141] 0.03805347 0.45856383 0.53985977 0.25433466 0.06188552 0.24144529 0.20751922
[148] 0.41818273 0.41818273 0.32671443 0.32671443 0.22416447 0.02768859 0.02768859
[155] 0.02768859 0.02768859 0.37292251 0.27835542 0.34663838 0.10643186 0.10643186
[162] 0.10643186 0.33999494 0.34418321 0.34418321 0.34418321 0.29464060 0.29464060
[169] 0.17753921 0.17753921 0.30720547 -0.10340773 0.34292164 0.34292164 0.29268587
[176] 0.29268587 0.32356194 0.32356194 0.32356194 0.32356194 0.32356194 0.32356194
[183] 0.32356194 0.25023824 0.56963789 0.56963789 0.26866996 0.24003431 0.24003431
[190] 0.24003431 0.30651307 0.30651307 0.30651307 0.30117783 0.22754295 0.22754295
[197] 0.22754295 0.19118901 0.34882855 0.15409377 0.35584110 0.35584110 0.37491578
[204] 0.61399084 0.61399084 0.24176238 0.21764882 0.43872434 0.14505404 0.14505404
[211] 0.14505404 0.02978949 0.02978949 0.08431564 0.29262277 0.29262277 0.38905174
[218] 0.38905174 0.38905174 0.38905174 0.42026922 0.42026922 0.30235702 0.22097413
[225] 0.12205401 0.12205401 0.17399822 0.61717242 0.61717242 0.61761999 0.61761999
[232] 0.61761999 0.27398288 0.61761999 0.61761999 0.61761999 0.19480835 0.26558110
[239] 0.19451952 0.41295883 0.26435342 0.32837936 0.32837936 0.26435342 0.26435342
[246] 0.32837936 0.24925001 0.20963798 0.20963798 0.13365066 0.13365066 0.29134080
[253] 0.33295015 0.33295015 0.38797778 0.47895822 0.22553048 0.22553048 0.68912876
[260] 0.41499376 0.39954847 0.27543253 0.39954847 0.33152789 0.33152789 0.22097413
[267] 0.50127369 0.50127369 0.28805068 0.23648851 0.33503899 0.33503899 0.13901943
[274] 0.43911955 0.34902355 0.34902355 0.17819393 0.17819393 0.37834024 0.55065745
[281] 0.55065745 0.55065745 0.55065745 0.36970979 0.36970979 0.36970979 0.28695902
[288] 0.28695902 0.39506948 0.53572440 0.38191035 0.43911955 0.43911955 0.39316535
[295] 0.39316535 0.44527993 0.44527993 0.24428052 0.24428052 0.24428052 0.26697093
[302] 0.26697093 0.26697093 0.35750881 0.64410371 0.64410371 0.39375058 0.26623076
[309] 0.13901943 0.55248690 0.55248690 0.48353609 0.20958337 0.20958337 0.20958337
[316] 0.42928597 0.55040950 0.55040950 0.42928597 0.28894442 0.25610211 0.25610211
[323] 0.25610211 0.11335804 0.11335804 0.33530894 0.33530894 0.33503899 0.20107158
[330] 0.20107158 -0.05549856 0.33295015 0.08869098 0.08869098 0.34864193 0.26524264
[337] 0.38271365 0.38271365 0.11906540 0.11906540 0.11906540 0.11906540 0.11906540
[344] 0.11906540 0.11906540 0.19296168 0.19296168 0.29257748 0.20451109 0.26075196
[351] 0.10638709 0.26075196 0.27759597 0.27759597 0.19800223 0.19800223 0.19800223
[358] 0.19800223 0.54940397 0.17737244 0.27315089 0.27315089 0.27315089 0.27315089
[365] 0.15025403 0.15025403 0.15025403 0.13972948 0.11975212 0.19480835 0.19480835
[372] 0.31679219 0.31679219 0.31679219 0.29135856 0.14708424 0.21194796 0.21194796
[379] 0.19480835 0.19480835 0.19480835 0.19480835 0.28232363 0.40382153 0.42492849
[386] 0.42492849 0.39606744 0.39606744 0.19545263 0.26866147 0.10061021 0.26284716
[393] 0.40532196 0.46191397 0.46191397 0.42251179 0.09479325 0.09479325 0.09479325
[400] 0.09479325 0.11760347 0.33932373 0.33932373 0.27632663 0.09752516 0.09752516
[407] 0.24807508 0.34536079 0.33065709 0.33065709 0.38341090 0.38341090 0.38341090
[414] 0.38341090 0.38341090 0.38341090 0.41908512 0.15868144 0.18290506 0.18290506
[421] 0.32473221 0.17621450 0.31852022 0.35420716 0.35420716 0.35420716 0.35420716
[428] 0.35420716 0.42635909 0.42635909 0.42635909 0.42635909 0.46519548 0.46519548
[435] 0.24742728 0.24742728 0.24742728 0.38648632 0.38648632 0.38648632 0.38648632
[442] 0.32494515 0.32494515 0.25818825 0.25818825 0.22183421 0.36074921 0.35419440
[449] 0.28232363 0.40000048 0.40000048 0.40000048 0.26866147 0.43936777 0.17097682
[456] 0.17097682 0.07296924 0.07296924 0.07252661 0.25975823 0.25975823 0.25975823
[463] 0.25975823 0.48333451 0.48333451 0.48333451 0.48333451 0.48333451 0.62933964
[470] 0.37921754 0.37921754 0.71599680 0.41851267 0.41851267 0.66637468 0.66637468
[477] 0.66637468 0.22065853 0.66637468 0.22065853 0.21561220 0.21228148 0.54442954
[484] 0.54442954 0.29771230 0.26977763 0.26977763 0.26977763 0.26977763 0.26977763
[491] 0.44605914 0.44605914 0.35467455 0.23727855 0.34414342 0.31451067 0.21009411
[498] 0.51107609 0.51107609 0.15329936 0.46742508 0.27574435 0.28788379 0.27944165
[505] 0.27944165 0.27944165 0.23276499 0.40663230 0.38478068 0.38478068 0.40663230
[512] 0.38478068 0.33832327 0.49188107 0.17188460 0.66733307 0.66733307 0.66733307
[519] 0.37924612 0.37924612 0.37924612 0.37924612 0.35296255 0.35296255 0.35296255
[526] 0.35296255 0.23239194 0.46783736 0.46783736 0.45933643 0.50037360 0.33256933
[533] 0.33256933 0.11522450 0.11522450 0.11522450 0.42348382 0.28213677 0.28213677
[540] 0.28213677 0.35237974 0.36962998 0.13348678 0.29510269 0.27379653 0.27379653
[547] 0.27379653 0.20024639 0.48435724 0.10413206 0.53894675 0.53894675 0.53894675
[554] 0.53894675 0.30058587 0.31918937 0.31918937 0.32310262 0.32152593 0.34089285
[561] 0.34089285 0.30032814 0.28670874 0.06072695 0.06072695 0.28510141 0.35474956
[568] 0.19084437 0.19084437 0.19084437 0.19084437 0.19084437 0.18399474 0.29962415
[575] 0.25871193 0.25871193 0.17043963 0.27061433 0.28246930 0.28246930 0.40231720
[582] 0.18399474 0.16565591 0.16565591 0.16565591 0.13141419 0.13141419 0.13141419
[589] 0.13141419 0.60347027 0.34893918 0.47167325 0.47167325 0.01942461 0.35959858
[596] 0.16684429 0.28582069 0.16684429 0.34378025 0.16684429 0.16684429 0.16684429
[603] 0.29218605 0.49606678 0.38980836 0.13688324 0.13688324 0.13688324 0.36810336
[610] 0.39171976 0.12634233 0.12634233 0.35959858 0.46148670 0.46148670 0.36722207
[617] 0.12880483 0.16684429 0.16684429 0.16684429 0.16684429 0.16684429 0.16684429
[624] 0.28002286 0.28002286 0.36554015 0.36554015 0.09121583 0.23700006 0.45689741
[631] 0.30840921 0.38239518 0.38239518 0.38239518 0.15650846 0.17676929 0.16684429
[638] 0.11087986 0.11087986 0.11087986 0.29198942 0.29198942 0.29198942 0.04749797
[645] 0.04749797 0.29672232 0.29672232 0.29672232 0.34986126 0.34986126 0.36937299
[652] 0.36937299 0.36937299 0.36937299 0.36937299 0.36937299 0.51058239 0.51058239
[659] 0.51058239 0.17936820 0.36287007 0.43324155 0.41364384 0.29646480 0.29646480
[666] 0.27581954 0.27581954 0.20932880 0.20932880 0.32480201 0.23700006 0.33134106
[673] 0.34374881 0.34374881 0.36502451 0.43573818 0.43573818 0.30863211 0.29766911
[680] 0.29766911 0.29766911 0.29766911 0.23220378 0.23220378 0.10948990 0.16639279
[687] 0.32022968 0.32022968 0.44445503 0.44445503 0.44445503 0.24001507 0.21241847
[694] 0.62469459 0.48886958 0.48886958 0.60914916 0.60914916 0.60914916 0.60914916
[701] 0.60914916 0.60914916 0.27718124 0.43350253 0.31388971 0.28003451 0.28003451
[708] 0.28003451 0.31388971 0.31388971 0.27028918 0.30839357 0.30839357 0.23635405
[715] 0.23635405 0.23635405 0.23635405 0.23635405 0.46078876 0.46078876 0.29305270
[722] 0.18662442 0.29305270 0.18662442 0.29265600 0.42247012 0.26275879 0.27063087
[729] 0.27063087 0.20421600 0.20421600 0.32550704 0.14198416 0.43834126 0.43834126
[736] 0.23906977 0.41688514 0.22830476 0.22830476 0.13620186 0.13620186 0.38084701
[743] 0.42326534 0.38084701 0.38084701 0.42326534 0.62469459 0.22805996 0.58255124
[750] 0.58255124 0.48922512 0.48306322 0.65636390 0.43135113 0.43135113 0.53997517
[757] 0.53997517 0.53997517 0.27382818 0.27382818 0.29063261 0.35043168 0.35043168
[764] 0.29956141 0.29956141 0.26275879 0.16661555 0.15290502 0.52164483 0.49754068
[771] 0.52164483 0.52164483 0.52164483 0.52164483 0.52164483 0.26869833 0.26441982
[778] 0.21834098 0.14689450 0.21398099 0.38635373 0.40625045 0.30499524 0.30499524
[785] 0.21241847 0.15666665 0.15666665 0.15666665 0.15666665 0.15666665 0.15666665
[792] 0.18336067 0.18336067 0.34033900 0.18336067 0.18336067 0.26733646 0.25249553
[799] 0.38293645 0.38293645 0.30867067 0.30867067 0.45520896 0.45520896 0.21571207
[806] 0.10628872 0.35442668 0.35442668 0.11720163 0.11720163 0.11720163 0.13411306
[813] 0.34707791 0.34707791 0.25326529 0.33024609 0.15666665 0.15666665 0.26869833
[820] 0.35495070 0.35495070 0.45061484 0.13543127 0.13543127 0.13543127 0.13543127
[827] 0.28002977 0.39779583 0.40002367 0.34416991 0.46875969 0.48659649 0.48659649
[834] 0.24494399 0.15902768 0.15902768 0.15902768 0.15902768 0.15902768 0.15902768
[841] 0.76887083 0.41143066 0.35945302 0.15400138 0.40757489 0.50589776 0.22135738
[848] 0.23845123 0.21666540 0.21666540 0.35878101 0.35878101 0.39463726 0.15913460
[855] 0.29372454 0.29372454 0.47654316 0.22844955 0.18296440 0.24166054 0.36712465
[862] 0.36712465 0.36712465 0.36712465 0.36712465 0.32095346 0.29861835 0.24549593
[869] 0.28002977 0.43365988 0.31240335 0.39788881 0.26709276 0.43918905 0.43918905
[876] 0.29433542 0.53798854 0.25488222 0.25488222 0.25488222 0.07561367 0.07561367
[883] 0.07561367 0.27713853 0.27713853 0.27713853 0.27713853 0.31658471 0.31658471pred_y =
case_when(
pred_test <= .1 ~ 0.1,
pred_test > .1 & pred_test <= .3 ~ 0.3,
pred_test > .3 & pred_test <= .5 ~ 0.5,
pred_test> .5 ~ 0.7,
TRUE ~ 0
)
pred_y [1] 0.5 0.5 0.5 0.5 0.5 0.7 0.7 0.7 0.3 0.7 0.7 0.7 0.3 0.5 0.3 0.5 0.5 0.3 0.3 0.3 0.5
[22] 0.5 0.5 0.3 0.5 0.7 0.5 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.5
[43] 0.5 0.5 0.5 0.7 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
[64] 0.3 0.5 0.7 0.3 0.3 0.3 0.3 0.5 0.5 0.3 0.3 0.3 0.5 0.5 0.5 0.7 0.7 0.7 0.7 0.7 0.5
[85] 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3
[106] 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.3 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.1 0.1 0.5
[127] 0.5 0.5 0.5 0.1 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.1 0.1 0.5 0.7 0.3 0.1 0.3 0.3
[148] 0.5 0.5 0.5 0.5 0.3 0.1 0.1 0.1 0.1 0.5 0.3 0.5 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3
[169] 0.3 0.3 0.5 0.1 0.5 0.5 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.7 0.7 0.3 0.3 0.3
[190] 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.5 0.3 0.5 0.5 0.5 0.7 0.7 0.3 0.3 0.5 0.3 0.3
[211] 0.3 0.1 0.1 0.1 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.7 0.7 0.7 0.7
[232] 0.7 0.3 0.7 0.7 0.7 0.3 0.3 0.3 0.5 0.3 0.5 0.5 0.3 0.3 0.5 0.3 0.3 0.3 0.3 0.3 0.3
[253] 0.5 0.5 0.5 0.5 0.3 0.3 0.7 0.5 0.5 0.3 0.5 0.5 0.5 0.3 0.7 0.7 0.3 0.3 0.5 0.5 0.3
[274] 0.5 0.5 0.5 0.3 0.3 0.5 0.7 0.7 0.7 0.7 0.5 0.5 0.5 0.3 0.3 0.5 0.7 0.5 0.5 0.5 0.5
[295] 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.7 0.7 0.5 0.3 0.3 0.7 0.7 0.5 0.3 0.3 0.3
[316] 0.5 0.7 0.7 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.3 0.3 0.1 0.5 0.1 0.1 0.5 0.3
[337] 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
[358] 0.3 0.7 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.3 0.3 0.3 0.3
[379] 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.1 0.1 0.1
[400] 0.1 0.3 0.5 0.5 0.3 0.1 0.1 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3
[421] 0.5 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.5 0.5 0.5 0.5
[442] 0.5 0.5 0.3 0.3 0.3 0.5 0.5 0.3 0.5 0.5 0.5 0.3 0.5 0.3 0.3 0.1 0.1 0.1 0.3 0.3 0.3
[463] 0.3 0.5 0.5 0.5 0.5 0.5 0.7 0.5 0.5 0.7 0.5 0.5 0.7 0.7 0.7 0.3 0.7 0.3 0.3 0.3 0.7
[484] 0.7 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.3 0.5 0.5 0.3 0.7 0.7 0.3 0.5 0.3 0.3 0.3
[505] 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.7 0.7 0.7 0.5 0.5 0.5 0.5 0.5 0.5 0.5
[526] 0.5 0.3 0.5 0.5 0.5 0.7 0.5 0.5 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.5 0.5 0.3 0.3 0.3 0.3
[547] 0.3 0.3 0.5 0.3 0.7 0.7 0.7 0.7 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.1 0.1 0.3 0.5
[568] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3
[589] 0.3 0.7 0.5 0.5 0.5 0.1 0.5 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.3 0.5 0.5 0.3 0.3 0.3 0.5
[610] 0.5 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.1 0.3 0.5
[631] 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.1 0.1 0.3 0.3 0.3 0.5 0.5 0.5
[652] 0.5 0.5 0.5 0.5 0.5 0.7 0.7 0.7 0.3 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.3 0.5
[673] 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.3 0.3
[694] 0.7 0.5 0.5 0.7 0.7 0.7 0.7 0.7 0.7 0.3 0.5 0.5 0.3 0.3 0.3 0.5 0.5 0.3 0.5 0.5 0.3
[715] 0.3 0.3 0.3 0.3 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.3 0.3 0.5 0.3 0.5 0.5
[736] 0.3 0.5 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.7 0.3 0.7 0.7 0.5 0.5 0.7 0.5 0.5 0.7
[757] 0.7 0.7 0.3 0.3 0.3 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.7 0.5 0.7 0.7 0.7 0.7 0.7 0.3 0.3
[778] 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.3
[799] 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.5 0.5 0.3 0.3 0.3 0.3 0.5 0.5 0.3 0.5 0.3 0.3 0.3
[820] 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3
[841] 0.7 0.5 0.5 0.3 0.5 0.7 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.5
[862] 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.5 0.5 0.5 0.3 0.5 0.5 0.3 0.7 0.3 0.3 0.3 0.1 0.1
[883] 0.1 0.3 0.3 0.3 0.3 0.5 0.5test_y=
case_when(
y_test <= .1 ~ 0.1,
y_test > .1 & y_test <= .3 ~ 0.3,
y_test > .3 & y_test <= .5 ~ 0.5,
y_test > .5 ~ 0.7,
TRUE ~ 0
)
test_y [1] 0.5 0.3 0.3 0.3 0.3 0.7 0.7 0.7 0.1 0.7 0.7 0.7 0.1 0.3 0.3 0.7 0.7 0.3 0.5 0.3 0.5
[22] 0.5 0.1 0.1 0.5 0.7 0.5 0.1 0.5 0.7 0.7 0.7 0.5 0.5 0.1 0.3 0.3 0.3 0.3 0.1 0.1 0.3
[43] 0.3 0.3 0.5 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.7 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3
[64] 0.1 0.5 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.1 0.1 0.5 0.5 0.5 0.5 0.5 0.5
[85] 0.3 0.5 0.5 0.5 0.7 0.1 0.1 0.7 0.1 0.1 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.1 0.1 0.1
[106] 0.1 0.1 0.1 0.1 0.1 0.1 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.1 0.3 0.3 0.3 0.1 0.1 0.7
[127] 0.1 0.5 0.5 0.1 0.1 0.3 0.3 0.1 0.1 0.1 0.1 0.3 0.5 0.1 0.1 0.3 0.7 0.3 0.1 0.5 0.1
[148] 0.5 0.5 0.3 0.3 0.5 0.1 0.1 0.1 0.1 0.7 0.1 0.5 0.3 0.3 0.3 0.5 0.7 0.7 0.7 0.3 0.3
[169] 0.1 0.1 0.7 0.1 0.3 0.3 0.1 0.1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.7 0.7 0.1 0.3 0.3
[190] 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.1 0.5 0.3 0.5 0.5 0.5 0.5 0.5 0.3 0.5 0.5 0.3 0.3
[211] 0.3 0.1 0.1 0.3 0.5 0.5 0.1 0.1 0.1 0.1 0.5 0.5 0.3 0.7 0.3 0.3 0.3 0.3 0.3 0.7 0.7
[232] 0.7 0.1 0.7 0.7 0.7 0.5 0.3 0.3 0.7 0.3 0.7 0.7 0.3 0.3 0.7 0.5 0.3 0.3 0.3 0.3 0.5
[253] 0.5 0.5 0.5 0.1 0.1 0.1 0.3 0.5 0.5 0.3 0.5 0.7 0.7 0.1 0.7 0.7 0.3 0.5 0.3 0.3 0.1
[274] 0.3 0.3 0.3 0.3 0.3 0.7 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.7 0.7 0.7 0.7 0.3 0.5 0.5 0.7
[295] 0.7 0.3 0.3 0.1 0.1 0.1 0.3 0.3 0.3 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.5 0.1 0.1 0.1
[316] 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.1 0.1 0.7 0.3 0.1 0.1 0.1 0.3
[337] 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.1 0.1 0.1 0.3 0.1 0.3 0.1 0.3 0.3 0.1 0.1 0.1
[358] 0.1 0.7 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.7 0.3 0.3 0.3 0.3 0.3 0.7 0.5 0.1 0.1
[379] 0.1 0.1 0.3 0.1 0.3 0.3 0.5 0.5 0.3 0.3 0.7 0.3 0.1 0.1 0.5 0.5 0.5 0.5 0.1 0.1 0.1
[400] 0.1 0.3 0.7 0.7 0.1 0.3 0.3 0.3 0.5 0.3 0.3 0.7 0.7 0.7 0.7 0.7 0.7 0.5 0.5 0.1 0.1
[421] 0.7 0.1 0.3 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.7 0.7 0.3 0.3 0.3 0.3 0.3 0.3 0.3
[442] 0.5 0.5 0.3 0.3 0.1 0.3 0.5 0.3 0.5 0.5 0.5 0.3 0.5 0.3 0.3 0.1 0.1 0.5 0.1 0.1 0.1
[463] 0.1 0.5 0.5 0.5 0.5 0.5 0.7 0.3 0.3 0.5 0.3 0.3 0.7 0.7 0.7 0.1 0.7 0.1 0.5 0.3 0.7
[484] 0.7 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.1 0.1 0.3 0.7 0.7 0.1 0.7 0.3 0.5 0.3
[505] 0.3 0.3 0.1 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.7 0.7 0.7 0.5 0.5 0.5 0.5 0.5 0.5 0.5
[526] 0.5 0.7 0.3 0.3 0.5 0.7 0.3 0.3 0.1 0.1 0.1 0.3 0.3 0.3 0.3 0.5 0.7 0.1 0.3 0.5 0.5
[547] 0.5 0.3 0.3 0.1 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.1 0.1 0.1 0.1 0.5
[568] 0.3 0.3 0.3 0.3 0.3 0.1 0.3 0.3 0.3 0.3 0.1 0.1 0.1 0.5 0.3 0.3 0.3 0.3 0.1 0.1 0.1
[589] 0.1 0.7 0.1 0.5 0.5 0.1 0.1 0.3 0.3 0.1 0.3 0.1 0.1 0.1 0.3 0.3 0.5 0.1 0.1 0.1 0.3
[610] 0.3 0.1 0.1 0.1 0.5 0.5 0.3 0.5 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.5 0.5 0.3 0.5 0.3
[631] 0.3 0.7 0.7 0.7 0.5 0.1 0.3 0.1 0.1 0.1 0.7 0.7 0.7 0.1 0.1 0.3 0.3 0.3 0.1 0.1 0.5
[652] 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.5 0.3 0.7 0.1 0.1 0.1 0.3 0.3 0.3 0.3 0.5 0.1 0.7
[673] 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.1 0.5 0.1 0.1 0.5 0.5 0.5 0.3 0.3
[694] 0.5 0.3 0.3 0.7 0.7 0.7 0.7 0.7 0.7 0.3 0.7 0.3 0.7 0.7 0.7 0.3 0.3 0.5 0.3 0.3 0.3
[715] 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.1 0.1 0.5 0.5 0.3 0.3 0.5 0.1 0.7 0.7
[736] 0.3 0.1 0.3 0.3 0.1 0.1 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.7 0.7 0.5 0.5 0.7 0.7 0.7 0.5
[757] 0.5 0.5 0.1 0.1 0.3 0.5 0.5 0.1 0.1 0.3 0.5 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
[778] 0.3 0.1 0.7 0.1 0.3 0.1 0.1 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.1 0.1 0.1 0.1 0.1 0.3 0.3
[799] 0.1 0.1 0.3 0.3 0.5 0.5 0.1 0.3 0.3 0.3 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.3
[820] 0.3 0.3 0.5 0.1 0.1 0.1 0.1 0.3 0.3 0.7 0.5 0.5 0.5 0.5 0.5 0.1 0.1 0.1 0.1 0.1 0.1
[841] 0.7 0.5 0.5 0.3 0.5 0.7 0.5 0.3 0.1 0.1 0.3 0.3 0.5 0.5 0.3 0.3 0.7 0.1 0.1 0.3 0.3
[862] 0.3 0.3 0.3 0.3 0.5 0.3 0.1 0.7 0.7 0.3 0.5 0.3 0.5 0.5 0.3 0.5 0.3 0.3 0.3 0.1 0.1
[883] 0.1 0.1 0.1 0.1 0.1 0.5 0.5pred_y = as.factor(pred_y)
print(pred_y) [1] 0.5 0.5 0.5 0.5 0.5 0.7 0.7 0.7 0.3 0.7 0.7 0.7 0.3 0.5 0.3 0.5 0.5 0.3 0.3 0.3 0.5
[22] 0.5 0.5 0.3 0.5 0.7 0.5 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.5
[43] 0.5 0.5 0.5 0.7 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
[64] 0.3 0.5 0.7 0.3 0.3 0.3 0.3 0.5 0.5 0.3 0.3 0.3 0.5 0.5 0.5 0.7 0.7 0.7 0.7 0.7 0.5
[85] 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3
[106] 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.3 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.1 0.1 0.5
[127] 0.5 0.5 0.5 0.1 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.1 0.1 0.5 0.7 0.3 0.1 0.3 0.3
[148] 0.5 0.5 0.5 0.5 0.3 0.1 0.1 0.1 0.1 0.5 0.3 0.5 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3
[169] 0.3 0.3 0.5 0.1 0.5 0.5 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.7 0.7 0.3 0.3 0.3
[190] 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.5 0.3 0.5 0.5 0.5 0.7 0.7 0.3 0.3 0.5 0.3 0.3
[211] 0.3 0.1 0.1 0.1 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.7 0.7 0.7 0.7
[232] 0.7 0.3 0.7 0.7 0.7 0.3 0.3 0.3 0.5 0.3 0.5 0.5 0.3 0.3 0.5 0.3 0.3 0.3 0.3 0.3 0.3
[253] 0.5 0.5 0.5 0.5 0.3 0.3 0.7 0.5 0.5 0.3 0.5 0.5 0.5 0.3 0.7 0.7 0.3 0.3 0.5 0.5 0.3
[274] 0.5 0.5 0.5 0.3 0.3 0.5 0.7 0.7 0.7 0.7 0.5 0.5 0.5 0.3 0.3 0.5 0.7 0.5 0.5 0.5 0.5
[295] 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.7 0.7 0.5 0.3 0.3 0.7 0.7 0.5 0.3 0.3 0.3
[316] 0.5 0.7 0.7 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.3 0.3 0.1 0.5 0.1 0.1 0.5 0.3
[337] 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
[358] 0.3 0.7 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.3 0.3 0.3 0.3
[379] 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.1 0.1 0.1
[400] 0.1 0.3 0.5 0.5 0.3 0.1 0.1 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3
[421] 0.5 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.5 0.5 0.5 0.5
[442] 0.5 0.5 0.3 0.3 0.3 0.5 0.5 0.3 0.5 0.5 0.5 0.3 0.5 0.3 0.3 0.1 0.1 0.1 0.3 0.3 0.3
[463] 0.3 0.5 0.5 0.5 0.5 0.5 0.7 0.5 0.5 0.7 0.5 0.5 0.7 0.7 0.7 0.3 0.7 0.3 0.3 0.3 0.7
[484] 0.7 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.3 0.5 0.5 0.3 0.7 0.7 0.3 0.5 0.3 0.3 0.3
[505] 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.7 0.7 0.7 0.5 0.5 0.5 0.5 0.5 0.5 0.5
[526] 0.5 0.3 0.5 0.5 0.5 0.7 0.5 0.5 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.5 0.5 0.3 0.3 0.3 0.3
[547] 0.3 0.3 0.5 0.3 0.7 0.7 0.7 0.7 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.1 0.1 0.3 0.5
[568] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3
[589] 0.3 0.7 0.5 0.5 0.5 0.1 0.5 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.3 0.5 0.5 0.3 0.3 0.3 0.5
[610] 0.5 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.1 0.3 0.5
[631] 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.1 0.1 0.3 0.3 0.3 0.5 0.5 0.5
[652] 0.5 0.5 0.5 0.5 0.5 0.7 0.7 0.7 0.3 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.3 0.5
[673] 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.3 0.3
[694] 0.7 0.5 0.5 0.7 0.7 0.7 0.7 0.7 0.7 0.3 0.5 0.5 0.3 0.3 0.3 0.5 0.5 0.3 0.5 0.5 0.3
[715] 0.3 0.3 0.3 0.3 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.3 0.3 0.5 0.3 0.5 0.5
[736] 0.3 0.5 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.7 0.3 0.7 0.7 0.5 0.5 0.7 0.5 0.5 0.7
[757] 0.7 0.7 0.3 0.3 0.3 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.7 0.5 0.7 0.7 0.7 0.7 0.7 0.3 0.3
[778] 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.3
[799] 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.5 0.5 0.3 0.3 0.3 0.3 0.5 0.5 0.3 0.5 0.3 0.3 0.3
[820] 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3
[841] 0.7 0.5 0.5 0.3 0.5 0.7 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.5
[862] 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.5 0.5 0.5 0.3 0.5 0.5 0.3 0.7 0.3 0.3 0.3 0.1 0.1
[883] 0.1 0.3 0.3 0.3 0.3 0.5 0.5
Levels: 0.1 0.3 0.5 0.7test_y = as.factor(test_y)
print(test_y) [1] 0.5 0.3 0.3 0.3 0.3 0.7 0.7 0.7 0.1 0.7 0.7 0.7 0.1 0.3 0.3 0.7 0.7 0.3 0.5 0.3 0.5
[22] 0.5 0.1 0.1 0.5 0.7 0.5 0.1 0.5 0.7 0.7 0.7 0.5 0.5 0.1 0.3 0.3 0.3 0.3 0.1 0.1 0.3
[43] 0.3 0.3 0.5 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.7 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3
[64] 0.1 0.5 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.1 0.1 0.5 0.5 0.5 0.5 0.5 0.5
[85] 0.3 0.5 0.5 0.5 0.7 0.1 0.1 0.7 0.1 0.1 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.1 0.1 0.1
[106] 0.1 0.1 0.1 0.1 0.1 0.1 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.1 0.3 0.3 0.3 0.1 0.1 0.7
[127] 0.1 0.5 0.5 0.1 0.1 0.3 0.3 0.1 0.1 0.1 0.1 0.3 0.5 0.1 0.1 0.3 0.7 0.3 0.1 0.5 0.1
[148] 0.5 0.5 0.3 0.3 0.5 0.1 0.1 0.1 0.1 0.7 0.1 0.5 0.3 0.3 0.3 0.5 0.7 0.7 0.7 0.3 0.3
[169] 0.1 0.1 0.7 0.1 0.3 0.3 0.1 0.1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.7 0.7 0.1 0.3 0.3
[190] 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.1 0.5 0.3 0.5 0.5 0.5 0.5 0.5 0.3 0.5 0.5 0.3 0.3
[211] 0.3 0.1 0.1 0.3 0.5 0.5 0.1 0.1 0.1 0.1 0.5 0.5 0.3 0.7 0.3 0.3 0.3 0.3 0.3 0.7 0.7
[232] 0.7 0.1 0.7 0.7 0.7 0.5 0.3 0.3 0.7 0.3 0.7 0.7 0.3 0.3 0.7 0.5 0.3 0.3 0.3 0.3 0.5
[253] 0.5 0.5 0.5 0.1 0.1 0.1 0.3 0.5 0.5 0.3 0.5 0.7 0.7 0.1 0.7 0.7 0.3 0.5 0.3 0.3 0.1
[274] 0.3 0.3 0.3 0.3 0.3 0.7 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.7 0.7 0.7 0.7 0.3 0.5 0.5 0.7
[295] 0.7 0.3 0.3 0.1 0.1 0.1 0.3 0.3 0.3 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.5 0.1 0.1 0.1
[316] 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.1 0.1 0.7 0.3 0.1 0.1 0.1 0.3
[337] 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.1 0.1 0.1 0.3 0.1 0.3 0.1 0.3 0.3 0.1 0.1 0.1
[358] 0.1 0.7 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.7 0.3 0.3 0.3 0.3 0.3 0.7 0.5 0.1 0.1
[379] 0.1 0.1 0.3 0.1 0.3 0.3 0.5 0.5 0.3 0.3 0.7 0.3 0.1 0.1 0.5 0.5 0.5 0.5 0.1 0.1 0.1
[400] 0.1 0.3 0.7 0.7 0.1 0.3 0.3 0.3 0.5 0.3 0.3 0.7 0.7 0.7 0.7 0.7 0.7 0.5 0.5 0.1 0.1
[421] 0.7 0.1 0.3 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.7 0.7 0.3 0.3 0.3 0.3 0.3 0.3 0.3
[442] 0.5 0.5 0.3 0.3 0.1 0.3 0.5 0.3 0.5 0.5 0.5 0.3 0.5 0.3 0.3 0.1 0.1 0.5 0.1 0.1 0.1
[463] 0.1 0.5 0.5 0.5 0.5 0.5 0.7 0.3 0.3 0.5 0.3 0.3 0.7 0.7 0.7 0.1 0.7 0.1 0.5 0.3 0.7
[484] 0.7 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.1 0.1 0.3 0.7 0.7 0.1 0.7 0.3 0.5 0.3
[505] 0.3 0.3 0.1 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.7 0.7 0.7 0.5 0.5 0.5 0.5 0.5 0.5 0.5
[526] 0.5 0.7 0.3 0.3 0.5 0.7 0.3 0.3 0.1 0.1 0.1 0.3 0.3 0.3 0.3 0.5 0.7 0.1 0.3 0.5 0.5
[547] 0.5 0.3 0.3 0.1 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.1 0.1 0.1 0.1 0.5
[568] 0.3 0.3 0.3 0.3 0.3 0.1 0.3 0.3 0.3 0.3 0.1 0.1 0.1 0.5 0.3 0.3 0.3 0.3 0.1 0.1 0.1
[589] 0.1 0.7 0.1 0.5 0.5 0.1 0.1 0.3 0.3 0.1 0.3 0.1 0.1 0.1 0.3 0.3 0.5 0.1 0.1 0.1 0.3
[610] 0.3 0.1 0.1 0.1 0.5 0.5 0.3 0.5 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.5 0.5 0.3 0.5 0.3
[631] 0.3 0.7 0.7 0.7 0.5 0.1 0.3 0.1 0.1 0.1 0.7 0.7 0.7 0.1 0.1 0.3 0.3 0.3 0.1 0.1 0.5
[652] 0.5 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.5 0.3 0.7 0.1 0.1 0.1 0.3 0.3 0.3 0.3 0.5 0.1 0.7
[673] 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.1 0.5 0.1 0.1 0.5 0.5 0.5 0.3 0.3
[694] 0.5 0.3 0.3 0.7 0.7 0.7 0.7 0.7 0.7 0.3 0.7 0.3 0.7 0.7 0.7 0.3 0.3 0.5 0.3 0.3 0.3
[715] 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.1 0.1 0.5 0.5 0.3 0.3 0.5 0.1 0.7 0.7
[736] 0.3 0.1 0.3 0.3 0.1 0.1 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.7 0.7 0.5 0.5 0.7 0.7 0.7 0.5
[757] 0.5 0.5 0.1 0.1 0.3 0.5 0.5 0.1 0.1 0.3 0.5 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
[778] 0.3 0.1 0.7 0.1 0.3 0.1 0.1 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.1 0.1 0.1 0.1 0.1 0.3 0.3
[799] 0.1 0.1 0.3 0.3 0.5 0.5 0.1 0.3 0.3 0.3 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.3
[820] 0.3 0.3 0.5 0.1 0.1 0.1 0.1 0.3 0.3 0.7 0.5 0.5 0.5 0.5 0.5 0.1 0.1 0.1 0.1 0.1 0.1
[841] 0.7 0.5 0.5 0.3 0.5 0.7 0.5 0.3 0.1 0.1 0.3 0.3 0.5 0.5 0.3 0.3 0.7 0.1 0.1 0.3 0.3
[862] 0.3 0.3 0.3 0.3 0.5 0.3 0.1 0.7 0.7 0.3 0.5 0.3 0.5 0.5 0.3 0.5 0.3 0.3 0.3 0.1 0.1
[883] 0.1 0.1 0.1 0.1 0.1 0.5 0.5
Levels: 0.1 0.3 0.5 0.7library(caret)
#Creates vectors having data points
expected_value <- factor(test_y)
predicted_value <- factor(pred_y)
#Creating confusion matrix
example <- confusionMatrix(data=predicted_value, reference = expected_value)
#Display results
exampleConfusion Matrix and Statistics
Reference
Prediction 0.1 0.3 0.5 0.7
0.1 29 4 1 1
0.3 163 180 44 15
0.5 34 120 161 50
0.7 0 11 31 45
Overall Statistics
Accuracy : 0.4668
95% CI : (0.4336, 0.5002)
No Information Rate : 0.3543
P-Value [Acc > NIR] : 3.662e-12
Kappa : 0.247
Mcnemar's Test P-Value : < 2.2e-16
Statistics by Class:
Class: 0.1 Class: 0.3 Class: 0.5 Class: 0.7
Sensitivity 0.12832 0.5714 0.6793 0.40541
Specificity 0.99095 0.6132 0.6871 0.94602
Pos Pred Value 0.82857 0.4478 0.4411 0.51724
Neg Pred Value 0.76932 0.7228 0.8550 0.91771
Prevalence 0.25422 0.3543 0.2666 0.12486
Detection Rate 0.03262 0.2025 0.1811 0.05062
Detection Prevalence 0.03937 0.4522 0.4106 0.09786
Balanced Accuracy 0.55963 0.5923 0.6832 0.67571importance =xgb.importance(model = model)print(xgb.plot.importance(importance_matrix = importance[1:9]))
# a plot with all the trees
xgb.plot.tree(model = model)
# this seems to be a mess. Hence, we only stick to 1 tree at a time. The below code is to plot first tree and show its node ID
xgb.plot.tree(model = model, trees = 0, show_node_id = TRUE)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