Laporan Pemodelan Statistical Machine Learning untuk Prediksi Harga AirBnb Listing
Metodologi Pemodelan
Random Forest merupakan pengembangan dari metode CART dengan menerapkan konsep bootstrap aggregating (bagging) dan random feature selection. Random forest merupakan metode yang dapat meningkatkan akurasi suatu klasfikasi data dari sebuah pemilah tunggal yang tidak stabil melalui kombinasi banyak pemilah dari suatu metode yang sama dengan proses voting untuk memperoleh prediksi klasifikasi akhir (Breiman 2001). Metode ini merupakan metode ensemble merupakan cara untuk meningkatkan akurasi metode klasifikasi dengan cara mengombinasikan metode klasifikasi (Han et al. 2011). Tahapan penyusunan random forest menurut Sartono dan Syafitri (2010) : 1. Pembentukan pohon melalui tahapan: a. Melakukan bootstrap, yakni menarik contoh acak disertai adanya pemulihan berukuran n dari data latih. b. Membuat pohon berdasarkan data yang digunakan pada tahap ini (random subsetting). Pada proses pemisahan harus dilakukan pemilihan secara acak m < d peubah penjelas dan lakukan pemisahan terbaik. c. Lakukan tahap a sampai b sebanyak k kali hingga diperoleh k buah pohon yang acak. 2. Membuat pendugaan gabungan sesuai k buah pohon tersebut. Majority vote untuk kasus klasifikasi, atau rata-rata pada kasus regresi. Tahapan dari pemodelan yang dilakukan adalah sebagai berikut:
Praproses Data
Praproses data bertujuan untuk membuat data menjadi data lengkap dan baik untuk dianalisis. Pada proposes data, sebelumnya akan dilihat terlebih dahulu data yang memiliki missing value lalu mengisi missing value tersebut dengan nilai rata-rata. Setelah itu, akan dilakukan pemilihan peubah bebas yang sekiranya berpengaruh terhadap peubah respon harga AirBnb listing
Load Package yang Dibutuhkan
Pertama, panggil package yang diperlukan dengan sintaks berikut
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.3 ✓ purrr 0.3.4
## ✓ tibble 3.1.0 ✓ dplyr 1.0.5
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.0## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()## Loading required package: mlr3## Loading required package: paradox##
## Attaching package: 'mice'## The following objects are masked from 'package:base':
##
## cbind, rbindLoad Dataset
Download data dan kemudian simpan pada direktori. Selanjutnya, panggil data tersebut menggunakan perintah read.csv pada package utils dan simpan pada objek train. Setelah itu lakukan pemilihan peubah yang memiliki hubungan dengan peubah respon harga, berdasarkan pertimbangan. Peubah yang terpilih ada sebanyak 15 yaitu room_type ,accommodates, bathrooms, bed_type ,cancellation_policy ,cleaning_fee ,city, host_has_profile_pic, host_identity_verified, host_response_rate ,instant_bookable ,number_of_reviews, review_scores_rating , bedrooms dan beds.
train<-utils::read.csv("Desktop/SEMESTER 1 & 2 S2/PMS/sta-582-pms-challange-part1/train.csv",sep=',',stringsAsFactors = T)
pmschallenge=train[,c(-11,-19, -20, -21, -25, -26,-4,-1,-2,-12,-18,-16,-22)]
head(pmschallenge)Periksa Missing Value
Gunakan sintaks berikut untuk melihat missing value.
## Warning in sorted_count(x): Variable contains value(s) of "" that have been
## converted to "empty".
## Warning in sorted_count(x): Variable contains value(s) of "" that have been
## converted to "empty".
## Warning in sorted_count(x): Variable contains value(s) of "" that have been
## converted to "empty".| Name | pmschallenge |
| Number of rows | 51879 |
| Number of columns | 16 |
| _______________________ | |
| Column type frequency: | |
| factor | 9 |
| numeric | 7 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| room_type | 0 | 1 | FALSE | 3 | Ent: 28966, Pri: 21423, Sha: 1490 |
| bed_type | 0 | 1 | FALSE | 5 | Rea: 50391, Fut: 549, Pul: 419, Air: 326 |
| cancellation_policy | 0 | 1 | FALSE | 5 | str: 22720, fle: 15741, mod: 13333, sup: 74 |
| cleaning_fee | 0 | 1 | FALSE | 2 | Tru: 38144, Fal: 13735 |
| city | 0 | 1 | FALSE | 6 | NYC: 22655, LA: 15733, SF: 4539, DC: 3947 |
| host_has_profile_pic | 0 | 1 | FALSE | 3 | t: 51592, f: 159, emp: 128 |
| host_identity_verified | 0 | 1 | FALSE | 3 | t: 34838, f: 16913, emp: 128 |
| host_response_rate | 0 | 1 | FALSE | 78 | 100: 30309, emp: 12754, 90%: 1590, 80%: 762 |
| instant_bookable | 0 | 1 | FALSE | 2 | f: 38325, t: 13554 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 |
|---|---|---|---|---|---|---|---|---|---|
| accommodates | 0 | 1.00 | 3.15 | 2.16 | 1 | 2 | 2 | 4 | 16 |
| bathrooms | 144 | 1.00 | 1.23 | 0.58 | 0 | 1 | 1 | 1 | 8 |
| number_of_reviews | 0 | 1.00 | 20.82 | 37.49 | 0 | 1 | 6 | 23 | 605 |
| review_scores_rating | 11621 | 0.78 | 94.08 | 7.82 | 20 | 92 | 96 | 100 | 100 |
| bedrooms | 66 | 1.00 | 1.26 | 0.85 | 0 | 1 | 1 | 1 | 10 |
| beds | 84 | 1.00 | 1.71 | 1.25 | 0 | 1 | 1 | 2 | 18 |
| price | 0 | 1.00 | 160.37 | 168.93 | 1 | 75 | 111 | 185 | 1999 |
## room_type accommodates bed_type cancellation_policy cleaning_fee city
## 40073 1 1 1 1 1 1
## 11563 1 1 1 1 1 1
## 102 1 1 1 1 1 1
## 9 1 1 1 1 1 1
## 20 1 1 1 1 1 1
## 17 1 1 1 1 1 1
## 11 1 1 1 1 1 1
## 18 1 1 1 1 1 1
## 45 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 2 1 1 1 1 1 1
## 4 1 1 1 1 1 1
## 12 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 0 0 0 0 0 0
## host_has_profile_pic host_identity_verified host_response_rate
## 40073 1 1 1
## 11563 1 1 1
## 102 1 1 1
## 9 1 1 1
## 20 1 1 1
## 17 1 1 1
## 11 1 1 1
## 18 1 1 1
## 45 1 1 1
## 1 1 1 1
## 2 1 1 1
## 4 1 1 1
## 12 1 1 1
## 1 1 1 1
## 1 1 1 1
## 0 0 0
## instant_bookable number_of_reviews price bedrooms beds bathrooms
## 40073 1 1 1 1 1 1
## 11563 1 1 1 1 1 1
## 102 1 1 1 1 1 0
## 9 1 1 1 1 1 0
## 20 1 1 1 1 0 1
## 17 1 1 1 1 0 1
## 11 1 1 1 1 0 0
## 18 1 1 1 1 0 0
## 45 1 1 1 0 1 1
## 1 1 1 1 0 1 1
## 2 1 1 1 0 1 0
## 4 1 1 1 0 0 1
## 12 1 1 1 0 0 1
## 1 1 1 1 0 0 0
## 1 1 1 1 0 0 0
## 0 0 0 66 84 144
## review_scores_rating
## 40073 1 0
## 11563 0 1
## 102 1 1
## 9 0 2
## 20 1 1
## 17 0 2
## 11 1 2
## 18 0 3
## 45 1 1
## 1 0 2
## 2 1 2
## 4 1 2
## 12 0 3
## 1 1 3
## 1 0 4
## 11621 11915Terlihat data memiliki sekitar 11 ribu missing value, yaitu 144 pada peubah bathrooms, 11621 pada peubah review_scores_rating, 66 pada peubah bedrooms, dan 84 pada peubah beds. Hal ini juga terlihat dari kotak merah yang terdapat pada output md.pattern()
Mengisi nilai missing value
karna peubah host_response_rate masih memiliki unsur “%” maaka hapus “%” tersebut menggunakan sintaks berikut karena hanya diperlukan angka numeric nya saja untuk analisis berikutnya. Setelah menghapus unsur “%” periksa kembali tipe data masing-masing peubah.
pmschallenge$host_response_rate=gsub('%','',pmschallenge$host_response_rate)
pmschallenge$host_response_rate=as.numeric(pmschallenge$host_response_rate)
str(pmschallenge)## 'data.frame': 51879 obs. of 16 variables:
## $ room_type : Factor w/ 3 levels "Entire home/apt",..: 1 1 1 1 1 2 1 1 2 2 ...
## $ accommodates : int 3 7 5 4 2 2 3 2 2 2 ...
## $ bathrooms : num 1 1 1 1 1 1 1 1 1 1 ...
## $ bed_type : Factor w/ 5 levels "Airbed","Couch",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ cancellation_policy : Factor w/ 5 levels "flexible","moderate",..: 3 3 2 1 2 3 2 2 2 2 ...
## $ cleaning_fee : Factor w/ 2 levels "False","True": 2 2 2 2 2 2 2 2 2 2 ...
## $ city : Factor w/ 6 levels "Boston","Chicago",..: 5 5 5 6 3 6 4 4 6 4 ...
## $ host_has_profile_pic : Factor w/ 3 levels "","f","t": 3 3 3 3 3 3 3 3 3 3 ...
## $ host_identity_verified: Factor w/ 3 levels "","f","t": 3 2 3 3 3 3 2 3 2 2 ...
## $ host_response_rate : num NA 100 100 NA 100 100 100 100 100 100 ...
## $ instant_bookable : Factor w/ 2 levels "f","t": 1 2 2 1 2 2 2 1 1 2 ...
## $ number_of_reviews : int 2 6 10 0 4 3 15 9 159 2 ...
## $ review_scores_rating : int 100 93 92 NA 40 100 97 93 99 90 ...
## $ bedrooms : int 1 3 1 2 0 1 1 1 1 1 ...
## $ beds : int 1 3 3 2 1 1 1 1 1 1 ...
## $ price : num 150 169 145 750 115 ...Selanjutnya, periksa nilai unik pada masing-masing peubah untuk mengetahui apakah terdapat nilai seperti “NA” ataupun “99” yang mengindikaskan missing value
## NULL## [1] Entire home/apt Private room Shared room
## Levels: Entire home/apt Private room Shared room## [1] 3 7 5 4 2 6 8 1 9 10 16 11 12 14 13 15## [1] Real Bed Futon Couch Pull-out Sofa Airbed
## Levels: Airbed Couch Futon Pull-out Sofa Real Bed## [1] strict moderate flexible super_strict_60
## [5] super_strict_30
## Levels: flexible moderate strict super_strict_30 super_strict_60## [1] True False
## Levels: False True## [1] NYC SF DC LA Chicago Boston
## Levels: Boston Chicago DC LA NYC SF## [1] t f
## Levels: f t## [1] t f
## Levels: f t## [1] f t
## Levels: f t## [1] 2 6 10 0 4 3 15 9 159 82 13 12 26 5 57 1 40 46
## [19] 17 138 11 29 18 31 19 25 22 23 28 7 32 144 16 14 105 59
## [37] 73 21 120 8 61 87 206 43 44 104 47 63 186 34 36 167 48 102
## [55] 67 81 58 72 38 68 79 98 30 187 123 70 55 54 27 52 42 60
## [73] 254 64 99 191 24 66 139 74 33 35 37 85 83 45 41 49 289 190
## [91] 62 20 78 127 118 216 135 69 51 77 181 53 101 106 114 110 56 192
## [109] 76 113 182 136 119 129 86 50 88 71 91 158 142 90 97 194 173 75
## [127] 39 132 112 161 208 111 116 148 89 156 150 145 84 258 178 155 163 149
## [145] 100 65 242 172 171 80 193 199 103 166 290 137 153 96 425 185 140 93
## [163] 360 143 107 214 141 246 217 195 196 109 273 251 305 133 351 189 269 134
## [181] 221 154 147 92 336 272 121 146 122 175 202 215 94 278 169 95 126 203
## [199] 108 130 256 323 115 128 469 160 124 220 165 131 287 152 492 224 201 170
## [217] 288 157 389 343 222 212 228 236 205 117 179 211 162 270 176 291 164 400
## [235] 125 318 252 183 268 174 298 260 322 207 197 378 279 219 329 349 213 267
## [253] 151 263 542 227 238 168 354 232 177 200 356 204 379 339 241 370 209 532
## [271] 188 231 248 495 311 326 281 240 255 292 257 226 218 180 275 282 304 237
## [289] 262 249 313 306 294 225 277 264 530 332 394 284 391 308 198 250 210 338
## [307] 451 274 605 247 453 385 283 229 302 344 266 295 335 271 244 184 285 239
## [325] 320 259 423 233 230 276 234 474 223 374 505 353 331 334 317 243 280 382
## [343] 380 358 265 253 296 337 341## [1] NA 100 71 68 67 83 50 90 86 92 80 89 93 99 0 96 94 91 25
## [20] 70 95 88 62 29 98 33 81 60 79 75 65 97 40 54 78 53 58 76
## [39] 63 82 87 64 20 77 38 41 59 57 30 85 56 42 44 35 14 27 10
## [58] 84 6 72 36 55 43 73 17 13 74 39 61 46 22 69 66 15 26 11
## [77] 52 21## [1] 150 169 145 750 115 85 83 120 36 100 70 200 142 75 99
## [16] 132 80 40 95 149 180 105 46 89 78 125 50 108 250 148
## [31] 325 35 116 119 141 350 1000 500 249 45 175 110 190 97 129
## [46] 48 260 60 220 122 138 65 55 479 700 280 450 379 130 159
## [61] 62 87 143 270 33 275 139 49 1275 850 77 255 161 90 264
## [76] 134 240 68 39 140 109 195 98 66 162 400 524 395 103 460
## [91] 170 385 43 289 59 198 42 359 92 185 208 179 52 1100 135
## [106] 310 155 102 30 375 800 290 58 111 950 160 29 88 38 79
## [121] 300 380 154 600 1938 199 299 259 205 86 399 698 599 81 152
## [136] 69 210 329 32 595 489 28 37 225 215 165 82 227 211 104
## [151] 247 355 495 57 245 492 124 213 279 1250 172 650 900 44 315
## [166] 320 74 204 999 285 295 72 96 219 41 67 25 235 63 201
## [181] 229 84 133 56 54 126 167 345 370 158 390 265 1095 91 118
## [196] 685 164 144 305 230 585 965 94 61 112 93 384 34 424 12
## [211] 47 171 147 21 746 256 127 182 123 261 550 18 1300 128 244
## [226] 24 349 23 239 107 114 232 223 64 1200 1350 189 267 121 1448
## [241] 194 151 71 995 465 184 699 181 525 425 156 361 76 137 1975
## [256] 1750 209 288 398 1400 777 499 136 725 324 216 178 51 258 990
## [271] 480 101 228 1057 214 197 163 20 429 1995 31 153 440 895 22
## [286] 269 53 381 449 438 246 340 174 27 339 177 970 1450 131 168
## [301] 830 15 571 278 513 651 73 19 365 858 207 248 1550 188 319
## [316] 106 242 117 649 470 1285 589 304 236 360 157 412 306 314 825
## [331] 445 330 1349 845 218 435 795 272 1050 222 212 588 348 276 469
## [346] 389 233 191 501 1800 436 341 455 113 580 1500 186 494 1667 785
## [361] 545 296 769 575 10 26 415 183 283 458 998 334 478 333 254
## [376] 419 773 382 516 309 477 1895 473 321 531 146 252 475 457 166
## [391] 224 443 672 192 316 377 974 459 749 238 298 1075 527 294 569
## [406] 715 221 1395 838 391 504 226 268 1499 291 217 193 297 1610 414
## [421] 490 540 799 625 327 357 292 695 815 1150 1295 176 1 403 253
## [436] 1460 405 196 624 432 257 911 17 997 548 366 1299 437 1225 564
## [451] 762 680 420 284 567 559 670 860 374 410 392 312 439 549 271
## [466] 775 173 485 369 536 307 560 555 337 1700 16 1889 344 780 570
## [481] 740 867 448 331 710 630 579 985 975 849 684 393 372 590 203
## [496] 187 783 266 462 872 417 263 277 496 645 1600 535 1950 409 620
## [511] 899 770 14 352 206 662 1195 949 1900 618 820 311 354 675 328
## [526] 529 303 287 234 875 1850 388 318 502 1010 1120 402 596 1731 841
## [541] 376 940 865 282 835 488 1371 510 928 518 237 1002 351 251 903
## [556] 530 591 925 520 301 851 1040 544 335 565 1020 735 474 505 539
## [571] 922 347 1707 358 1099 308 231 743 281 408 980 336 1094 1675 1799
## [586] 690 790 1080 1223 482 368 720 274 728 869 13 689 514 383 286
## [601] 859 346 629 1999 610 818 1498 714 1595 506 679 243 603 1368 293
## [616] 1705 745 597 551 413 356 442 343 993 913 202 1990 241 451 890
## [631] 411 637 935 558 1274 1090 1495 394 430 497 945 273 509 987 976
## [646] 798 371 317 1650 1570 839 844 519 713 1399 363 694 1980 313 1795
## [661] 486 739 433 441 322 640 522 547 362 407 1240 453 332 1749 927
## [676] 989 1168 615 326 1180 467 855 364 338 466 1125 515 824 1599## [1] 1 3 2 4 6 5 NA 10 16 13 7 8 12 11 14 9 15 0 18## [1] 1 3 2 0 4 5 NA 6 7 8 9 10## [1] 100 93 92 NA 40 97 99 90 89 91 88 86 72 98 95 96 84 80 94
## [20] 87 60 75 20 76 85 83 82 78 73 67 71 77 81 70 79 68 66 74
## [39] 63 50 53 65 27 64 69 30 58 62 49 57 54 47 56 55## [1] 1.0 1.5 2.0 NA 2.5 3.0 0.5 4.5 5.0 4.0 3.5 0.0 5.5 7.5 6.0 8.0 7.0 6.5Terlihat dari hasil diatas bahwa peubah host_response_rate, bedrooms, beds,bathrooms dan review_scores_rating memiliki missing value. Setelah itu, lakukan pengisian missing value dengan rata-ratanya pada peubah host_response_rate, bedrooms, beds,bathrooms dan review_scores_rating. Untuk melihat rata-rata peubah dapat dilihat dari fungsi summary() atau dari menghitung manual menggunakan mean().
## room_type accommodates bathrooms bed_type
## Entire home/apt:28966 Min. : 1.00 Min. :0.000 Airbed : 326
## Private room :21423 1st Qu.: 2.00 1st Qu.:1.000 Couch : 194
## Shared room : 1490 Median : 2.00 Median :1.000 Futon : 549
## Mean : 3.15 Mean :1.234 Pull-out Sofa: 419
## 3rd Qu.: 4.00 3rd Qu.:1.000 Real Bed :50391
## Max. :16.00 Max. :8.000
## NA's :144
## cancellation_policy cleaning_fee city host_has_profile_pic
## flexible :15741 False:13735 Boston : 2432 : 128
## moderate :13333 True :38144 Chicago: 2573 f: 159
## strict :22720 DC : 3947 t:51592
## super_strict_30: 74 LA :15733
## super_strict_60: 11 NYC :22655
## SF : 4539
##
## host_identity_verified host_response_rate instant_bookable number_of_reviews
## : 128 Min. : 0.00 f:38325 Min. : 0.00
## f:16913 1st Qu.:100.00 t:13554 1st Qu.: 1.00
## t:34838 Median :100.00 Median : 6.00
## Mean : 94.32 Mean : 20.82
## 3rd Qu.:100.00 3rd Qu.: 23.00
## Max. :100.00 Max. :605.00
## NA's :12754
## review_scores_rating bedrooms beds price
## Min. : 20.00 Min. : 0.000 Min. : 0.000 Min. : 1.0
## 1st Qu.: 92.00 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 75.0
## Median : 96.00 Median : 1.000 Median : 1.000 Median : 111.0
## Mean : 94.08 Mean : 1.261 Mean : 1.707 Mean : 160.4
## 3rd Qu.:100.00 3rd Qu.: 1.000 3rd Qu.: 2.000 3rd Qu.: 185.0
## Max. :100.00 Max. :10.000 Max. :18.000 Max. :1999.0
## NA's :11621 NA's :66 NA's :84## [1] 94.31614is.na_replace_host_response_rate <- pmschallenge$host_response_rate # Duplicate first column
is.na_replace_host_response_rate[is.na(is.na_replace_host_response_rate)] <- 94.32
pmschallenge$host_response_rate=is.na_replace_host_response_rate
#bedrooms
is.na_replace_bedrooms <- pmschallenge$bedrooms # Duplicate first column
is.na_replace_bedrooms[is.na(is.na_replace_bedrooms)] <- 1.261
pmschallenge$bedrooms=is.na_replace_bedrooms
#beds
is.na_replace_beds <- pmschallenge$beds # Duplicate first column
is.na_replace_beds[is.na(is.na_replace_beds)] <- 1.708
pmschallenge$beds=is.na_replace_beds
#bathrooms
is.na_replace_bathrooms<- pmschallenge$bathrooms # Duplicate first column
is.na_replace_bathrooms[is.na(is.na_replace_bathrooms)] <- 1.233
pmschallenge$bathrooms=is.na_replace_bathrooms
#scores
is.na_replace_scores <- pmschallenge$review_scores_rating # Duplicate first column
is.na_replace_scores[is.na(is.na_replace_scores)] <- 94.08
pmschallenge$review_scores_rating=is.na_replace_scores
library(mice)
md.pattern(pmschallenge)## /\ /\
## { `---' }
## { O O }
## ==> V <== No need for mice. This data set is completely observed.
## \ \|/ /
## `-----'
## room_type accommodates bathrooms bed_type cancellation_policy
## 51879 1 1 1 1 1
## 0 0 0 0 0
## cleaning_fee city host_has_profile_pic host_identity_verified
## 51879 1 1 1 1
## 0 0 0 0
## host_response_rate instant_bookable number_of_reviews
## 51879 1 1 1
## 0 0 0
## review_scores_rating bedrooms beds price
## 51879 1 1 1 1 0
## 0 0 0 0 0Setelah data diisi dengan missing value, terlihat pada output md.pattern tidak ada kotak merah yang mengindikasikan data sudah bersih dari missing value
Praproses untuk Dataset Test
Ulangi langkah-langkah sebelumnya, mulai dari load dataset hingga pengisian missing value.
test1<-utils::read.csv("Desktop/SEMESTER 1 & 2 S2/PMS/sta-582-pms-challange-part1/test.csv",sep=',',stringsAsFactors = T)
test=test1[,c(-11,-19, -20, -21, -25,-2, -26,-4,-1,-12,-18,-16,-22)]
skim_without_charts(data = test) #bs liat mean lgsg bedrooms beds bathrooms review_scores_rating## Warning in sorted_count(x): Variable contains value(s) of "" that have been
## converted to "empty".
## Warning in sorted_count(x): Variable contains value(s) of "" that have been
## converted to "empty".
## Warning in sorted_count(x): Variable contains value(s) of "" that have been
## converted to "empty".| Name | test |
| Number of rows | 22232 |
| Number of columns | 15 |
| _______________________ | |
| Column type frequency: | |
| factor | 9 |
| numeric | 6 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| room_type | 0 | 1 | FALSE | 3 | Ent: 12344, Pri: 9215, Sha: 673 |
| bed_type | 0 | 1 | FALSE | 5 | Rea: 21637, Fut: 204, Pul: 166, Air: 151 |
| cancellation_policy | 0 | 1 | FALSE | 5 | str: 9654, fle: 6804, mod: 5730, sup: 38 |
| cleaning_fee | 0 | 1 | FALSE | 2 | Tru: 16259, Fal: 5973 |
| city | 0 | 1 | FALSE | 6 | NYC: 9694, LA: 6720, SF: 1895, DC: 1741 |
| host_has_profile_pic | 0 | 1 | FALSE | 3 | t: 22105, f: 67, emp: 60 |
| host_identity_verified | 0 | 1 | FALSE | 3 | t: 14910, f: 7262, emp: 60 |
| host_response_rate | 0 | 1 | FALSE | 72 | 100: 12945, emp: 5545, 90%: 687, 80%: 351 |
| instant_bookable | 0 | 1 | FALSE | 2 | f: 16335, t: 5897 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 |
|---|---|---|---|---|---|---|---|---|---|
| accommodates | 0 | 1.00 | 3.17 | 2.15 | 1 | 2 | 2 | 4.00 | 16 |
| bathrooms | 56 | 1.00 | 1.24 | 0.58 | 0 | 1 | 1 | 1.00 | 8 |
| number_of_reviews | 0 | 1.00 | 21.09 | 38.61 | 0 | 1 | 5 | 23.25 | 525 |
| review_scores_rating | 5101 | 0.77 | 94.04 | 7.88 | 20 | 92 | 96 | 100.00 | 100 |
| bedrooms | 25 | 1.00 | 1.28 | 0.86 | 0 | 1 | 1 | 1.00 | 10 |
| beds | 47 | 1.00 | 1.72 | 1.26 | 0 | 1 | 1 | 2.00 | 16 |
## room_type accommodates bed_type cancellation_policy cleaning_fee city
## 17056 1 1 1 1 1 1
## 5074 1 1 1 1 1 1
## 36 1 1 1 1 1 1
## 5 1 1 1 1 1 1
## 14 1 1 1 1 1 1
## 8 1 1 1 1 1 1
## 7 1 1 1 1 1 1
## 7 1 1 1 1 1 1
## 13 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 5 1 1 1 1 1 1
## 5 1 1 1 1 1 1
## 1 1 1 1 1 1 1
## 0 0 0 0 0 0
## host_has_profile_pic host_identity_verified host_response_rate
## 17056 1 1 1
## 5074 1 1 1
## 36 1 1 1
## 5 1 1 1
## 14 1 1 1
## 8 1 1 1
## 7 1 1 1
## 7 1 1 1
## 13 1 1 1
## 1 1 1 1
## 5 1 1 1
## 5 1 1 1
## 1 1 1 1
## 0 0 0
## instant_bookable number_of_reviews bedrooms beds bathrooms
## 17056 1 1 1 1 1
## 5074 1 1 1 1 1
## 36 1 1 1 1 0
## 5 1 1 1 1 0
## 14 1 1 1 0 1
## 8 1 1 1 0 1
## 7 1 1 1 0 0
## 7 1 1 1 0 0
## 13 1 1 0 1 1
## 1 1 1 0 1 1
## 5 1 1 0 0 1
## 5 1 1 0 0 1
## 1 1 1 0 0 0
## 0 0 25 47 56
## review_scores_rating
## 17056 1 0
## 5074 0 1
## 36 1 1
## 5 0 2
## 14 1 1
## 8 0 2
## 7 1 2
## 7 0 3
## 13 1 1
## 1 0 2
## 5 1 2
## 5 0 3
## 1 0 4
## 5101 5229test$host_response_rate=gsub('%','',test$host_response_rate)
test$host_response_rate=as.numeric(test$host_response_rate)
str(test)## 'data.frame': 22232 obs. of 15 variables:
## $ room_type : Factor w/ 3 levels "Entire home/apt",..: 1 2 1 2 1 1 1 2 1 2 ...
## $ accommodates : int 4 2 4 2 4 6 4 8 2 1 ...
## $ bathrooms : num 1.5 1.5 1 1 1 1 1 1 1 1 ...
## $ bed_type : Factor w/ 5 levels "Airbed","Couch",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ cancellation_policy : Factor w/ 5 levels "flexible","moderate",..: 3 2 2 2 2 3 3 2 1 1 ...
## $ cleaning_fee : Factor w/ 2 levels "False","True": 2 2 2 2 2 2 2 2 1 1 ...
## $ city : Factor w/ 6 levels "Boston","Chicago",..: 4 2 4 4 3 5 4 4 5 5 ...
## $ host_has_profile_pic : Factor w/ 3 levels "","f","t": 3 3 3 3 3 3 3 3 3 3 ...
## $ host_identity_verified: Factor w/ 3 levels "","f","t": 3 3 3 3 2 3 3 3 2 2 ...
## $ host_response_rate : num 100 100 100 NA NA 100 100 100 100 NA ...
## $ instant_bookable : Factor w/ 2 levels "f","t": 1 1 2 1 1 1 1 2 1 1 ...
## $ number_of_reviews : int 29 0 73 2 0 14 248 44 0 0 ...
## $ review_scores_rating : int 97 NA 99 100 NA 100 96 92 NA NA ...
## $ bedrooms : int 2 1 1 1 1 3 2 1 1 1 ...
## $ beds : int 2 1 1 1 2 3 3 7 1 1 ...## NULL## [1] Entire home/apt Private room Shared room
## Levels: Entire home/apt Private room Shared room## [1] 4 2 6 8 1 5 3 7 9 10 16 12 11 13 15 14## [1] Real Bed Pull-out Sofa Futon Airbed Couch
## Levels: Airbed Couch Futon Pull-out Sofa Real Bed## [1] strict moderate flexible super_strict_30
## [5] super_strict_60
## Levels: flexible moderate strict super_strict_30 super_strict_60## [1] True False
## Levels: False True## [1] LA Chicago DC NYC Boston SF
## Levels: Boston Chicago DC LA NYC SF## [1] t f
## Levels: f t## [1] t f
## Levels: f t## [1] f t
## Levels: f t## [1] 29 0 73 2 14 248 44 34 85 1 5 30 4 3 6 10 7 38
## [19] 67 9 27 12 51 22 13 28 126 21 107 25 15 8 58 39 63 17
## [37] 78 31 75 128 90 135 11 55 18 43 19 125 129 24 318 33 23 26
## [55] 20 191 46 93 52 61 35 16 88 37 154 32 136 69 64 40 41 314
## [73] 112 56 117 255 66 91 62 114 84 146 74 54 47 57 87 53 42 45
## [91] 157 95 94 122 49 171 79 111 123 102 101 131 384 133 48 68 134 179
## [109] 71 147 59 202 148 120 98 156 200 60 89 159 113 267 144 207 36 82
## [127] 121 208 180 104 119 92 263 77 251 181 237 50 80 168 100 289 162 81
## [145] 76 118 139 65 86 103 83 72 152 105 317 218 145 140 158 143 188 383
## [163] 160 165 115 70 172 163 198 150 465 178 302 166 323 153 106 204 110 167
## [181] 170 480 97 142 225 108 220 138 99 192 109 193 306 303 215 127 281 327
## [199] 149 196 174 209 227 201 337 96 116 280 305 335 137 195 252 173 242 287
## [217] 214 366 304 226 194 203 249 234 155 212 262 151 311 253 367 184 449 124
## [235] 141 130 217 161 275 169 269 391 213 211 264 219 356 273 132 164 241 177
## [253] 298 229 232 185 296 260 309 182 210 328 206 176 230 197 286 297 189 347
## [271] 238 175 272 199 247 243 223 266 525 278 307 321 379 320 239 274 388 236
## [289] 235 224 308 378 216 246 256 222 354 376 339 294 231 315## [1] 100 NA 82 50 88 70 96 83 90 98 80 93 63 81 38 0 79 60 29
## [20] 89 78 75 86 94 97 87 92 95 67 99 30 40 17 33 68 20 54 73
## [39] 91 64 56 74 25 62 71 85 84 76 77 43 44 57 46 58 55 26 10
## [58] 52 23 72 65 69 22 59 36 41 53 35 42 31 14 47## [1] 2 1 3 7 4 5 6 8 9 12 NA 15 10 11 13 0 16 14## [1] 2 1 3 0 NA 4 5 6 7 9 10 8## [1] 97 NA 99 100 96 92 88 89 80 84 85 94 70 98 87 90 91 93 95
## [20] 20 82 75 55 73 81 83 86 60 79 47 74 78 68 40 77 76 50 67
## [39] 65 64 71 72 69 63 27 35 30 62## [1] 1.5 1.0 2.0 2.5 3.0 0.0 3.5 NA 4.5 0.5 4.0 5.0 6.5 8.0 5.5 6.0 7.0 7.5is.na_replace_host_response_rate <- test$host_response_rate # Duplicate first column
is.na_replace_host_response_rate[is.na(is.na_replace_host_response_rate)] <- 94.32
test$host_response_rate=is.na_replace_host_response_rate
#bedrooms
is.na_replace_bedrooms <- test$bedrooms # Duplicate first column
is.na_replace_bedrooms[is.na(is.na_replace_bedrooms)] <- 1.261
test$bedrooms=is.na_replace_bedrooms
#beds
is.na_replace_beds <- test$beds # Duplicate first column
is.na_replace_beds[is.na(is.na_replace_beds)] <- 1.708
test$beds=is.na_replace_beds
#bathrooms
is.na_replace_bathrooms<- test$bathrooms # Duplicate first column
is.na_replace_bathrooms[is.na(is.na_replace_bathrooms)] <- 1.233
test$bathrooms=is.na_replace_bathrooms
#scores
is.na_replace_scores <- test$review_scores_rating # Duplicate first column
is.na_replace_scores[is.na(is.na_replace_scores)] <- 94.08
test$review_scores_rating=is.na_replace_scoresEksplorasi Data
Pada eksplorasi data dilakukan pengamatan secara objektif untuk melihat karakteristik dan tipe data yang digunakan. Eksplorasi data ini bertujuan untuk mempelajari pola data agar nantinya menjadi informasi yang dapat menunjang analisis data. Dengan adanya eksplorasi data akan lebih mudah menentukan informasi yang akan disampaikan. Untuk melihat histogram dari peubah, gunakan sintaks berikut:
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Berdasarkan histogram tersebut, terlihat untuk peubah host_response_rate memiliki sebaran yang menjulur ke kiri, dimana modus pada peubah tersebut adalah sekitar 100% yang berarti tuan rumah pada situs Airbnb kebanyakan memiliki tingkat respon sekitar 100%. Begitu juga untuk peubah review_scores_rating yang memiliki sebaran menjulur ke kiri. Sementara itu, untuk peubah price yang memiliki sebaran menjulur ke kanan, dapat dilihat bahwa harga rumah pada situs Airbnb kebanyakan dibawah 250. Begitu juga untuk peubah number_of_reviews yang memiliki sebaran menjulur ke kanan juga.
Import data ke ekosistem mlr3
Gunakan sintaks berikut dimana id adalah ‘challenge’, backend adalah data yang ingin dimodelkan dengan catatan peubah respon-nya harus berupa peubah numerik yaitu “pmschallenge”, dan targetnya yaitu “price”.
Mendefinisikan Tuning Hiperparameter
Hiperparamter Random Forest yang digunakan adalah mtry yaitu banyaknya variabel yang digunakan untuk splitting pada tiap node dimana pada tahap ini digunakan nilai rentang 2 sampai 7, max.depth yaitu maksimal kedalaman pada tiap node di pohon final dimana pada tahap ini digunakan nilai renatng 1 sampai 30, num.trees yaitu banyaknya pohon, dimana pada tahap ini digunakan nilai rentang 1 sampai 30, min.node.size yaitu ukuran node paling minimum pada setiap terminal node dimana pada tahap ini digunakan 1 sampai 300, dan num.threads yaitu adalah jumlah utas cpu yang harus digunakan oleh ranger dimana pada tahap ini digunakan nilai rentang 1 sampai 200.
hiperparameter yang digunakan memiliki tipe data bilangan bulat sehingga hiperparameternya adalah fungsi ParamInt$new. Kemudian lower dan upper menunjukan batas bawah dan atas nilai hiperparamter yang akan ditelusuri. Dengan kata lain, penelusuran nilai akan dilakukan direntang tersebut.
model_rfpms <- lrn("regr.ranger",importance="impurity")
param_bound_rf <- ParamSet$new(params =
list(ParamInt$new("mtry",
lower = 2,
upper = 7),
ParamInt$new("max.depth",
lower = 1,
upper = 30), ParamInt$new("num.trees",
lower = 1,
upper = 30), ParamInt$new("min.node.size",
lower = 1,
upper = 300), ParamInt$new("num.threads",
lower = 1,
upper = 200)
)
)Menentukan Stopping Criteria
Penentuan stopping criteria dapat dilakukan dengan menggunakan fungsi trm dimana digunakan metode stopping criteria “evals” yang berarti stopping criteria yang dipilih adalah banyaknya iterasi yang mana tuning hiperparameter akan berhenti saat mencapai iterasi tertentu
Menentukan Metode Optimisasi
Fungsi tnr memiliki satu argumen utama yaitu nama algoritma tuningnya, pada tahap ini digunakan metode optimasi “random_search” yang akan memilih nilai hiperparameter secara acak dari selang yang sudah kita tentukan. Pemilihan random_search ini dikarenakan algoritma ini memilih secara acak nilai-nilai hiperparameter yang ingin dituning sehingga semua nilai hiperparameter memiliki peluang yang sama untuk terpilih.
Menentukan Metode Resampling (inner resampling)
Metode resampling yang biasanya digunakan adalah nested resampling atau nested-CV. Berikut adalah sintaksnya untuk penentuan inner resampling
Menggabungkan Informasi kedalam Fungsi Autotuner
Fungsi ini dibutuhkan untuk tahap pemodelan selanjutnya
model_rf_tune <- AutoTuner$new(learner = model_rfpms,
measure = msr("regr.mae"),
terminator = terminate,
resampling = resample_inner,
search_space = param_bound_rf,
tuner = tuner,
store_models = TRUE
)Measure yang dipilih adalah regr.maee hal ini berarti tuning hiperparameter dilakukan berdasarkan nilai MAE.
Menentukan Metode Resampling (outer resampling)
outer resampling juga digunakan untuk membandingkan model yang sudah dituning dengan model sebelum di tuning maupun model lainnya. Berikut sintaks yang digunakan:
Komparasi Model
Pada tahap ini akan membandingkan performa model hasil tuning dengan sebelum tuning.
model_pms <- list(
model_rfpms,
model_rf_tune
)
design <- benchmark_grid(tasks = task_pms,
learners = model_pms,
resamplings = resample_outer
)
lgr::get_logger("bbotk")$set_threshold("warn")
bmrpms = benchmark(design,store_models = TRUE)## INFO [16:27:36.220] [mlr3] Running benchmark with 10 resampling iterations
## INFO [16:27:36.555] [mlr3] Applying learner 'regr.ranger.tuned' on task 'challenge' (iter 4/5)
## INFO [16:27:36.967] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:27:36.975] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:27:37.433] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:27:37.885] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:27:38.409] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:27:38.865] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:27:39.316] [mlr3] Finished benchmark
## INFO [16:27:39.537] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:27:39.546] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:27:40.715] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:27:41.715] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:27:42.696] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:27:43.733] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:27:44.849] [mlr3] Finished benchmark
## INFO [16:27:44.997] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:27:45.006] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:27:45.208] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:27:45.415] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:27:45.633] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:27:45.829] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:27:46.186] [mlr3] Finished benchmark
## INFO [16:27:46.322] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:27:46.332] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:27:46.661] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:27:46.993] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:27:47.330] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:27:47.651] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:27:47.977] [mlr3] Finished benchmark
## INFO [16:27:48.105] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:27:48.113] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:27:48.309] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:27:48.505] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:27:48.698] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:27:48.891] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:27:49.080] [mlr3] Finished benchmark
## INFO [16:27:49.271] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:27:49.279] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:27:50.009] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:27:50.725] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:27:51.412] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:27:52.129] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:27:52.805] [mlr3] Finished benchmark
## INFO [16:27:52.931] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:27:52.943] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:27:53.127] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:27:53.317] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:27:53.508] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:27:53.703] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:27:53.894] [mlr3] Finished benchmark
## INFO [16:27:54.018] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:27:54.026] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:27:55.074] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:27:56.169] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:27:57.118] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:27:58.086] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:27:59.055] [mlr3] Finished benchmark
## INFO [16:27:59.184] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:27:59.193] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:27:59.359] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:27:59.526] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:27:59.709] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:27:59.892] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:28:00.085] [mlr3] Finished benchmark
## INFO [16:28:00.205] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:28:00.213] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:28:00.678] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:28:01.084] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:28:01.486] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:28:01.909] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:28:02.656] [mlr3] Finished benchmark
## INFO [16:28:02.795] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:28:02.806] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:28:03.333] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:28:03.837] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:28:04.301] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:28:04.775] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:28:05.359] [mlr3] Finished benchmark
## INFO [16:28:05.565] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:28:05.576] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:28:05.964] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:28:06.381] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:28:06.805] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:28:07.231] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:28:07.640] [mlr3] Finished benchmark
## INFO [16:28:07.785] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:28:07.794] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:28:07.997] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:28:08.197] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:28:08.449] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:28:08.651] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:28:08.866] [mlr3] Finished benchmark
## INFO [16:28:08.983] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:28:08.992] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:28:09.471] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:28:10.016] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:28:10.547] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:28:11.086] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:28:11.689] [mlr3] Finished benchmark
## INFO [16:28:12.091] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:28:12.107] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:28:12.367] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:28:12.588] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:28:12.789] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:28:12.992] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:28:13.193] [mlr3] Finished benchmark
## INFO [16:28:13.308] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:28:13.316] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:28:13.492] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:28:13.710] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:28:13.907] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:28:14.089] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:28:14.268] [mlr3] Finished benchmark
## INFO [16:28:14.383] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:28:14.392] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:28:14.610] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:28:14.819] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:28:15.052] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:28:15.321] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:28:15.568] [mlr3] Finished benchmark
## INFO [16:28:15.686] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:28:15.695] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:28:16.545] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:28:17.353] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:28:18.128] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:28:18.914] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:28:19.725] [mlr3] Finished benchmark
## INFO [16:28:19.849] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:28:19.858] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:28:21.254] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:28:22.549] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:28:23.659] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:28:24.769] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:28:26.402] [mlr3] Finished benchmark
## INFO [16:28:26.565] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:28:26.574] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:28:27.397] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:28:28.521] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:28:29.591] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:28:30.556] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:28:31.450] [mlr3] Finished benchmark
## INFO [16:28:32.832] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:28:55.203] [mlr3] Applying learner 'regr.ranger.tuned' on task 'challenge' (iter 2/5)
## INFO [16:28:55.327] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:28:55.338] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:28:55.817] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:28:56.320] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:28:56.917] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:28:57.470] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:28:57.939] [mlr3] Finished benchmark
## INFO [16:28:58.072] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:28:58.080] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:28:58.461] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:28:58.877] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:28:59.265] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:28:59.626] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:28:59.980] [mlr3] Finished benchmark
## INFO [16:29:00.106] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:00.114] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:01.108] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:01.798] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:02.384] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:02.941] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:03.464] [mlr3] Finished benchmark
## INFO [16:29:03.592] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:03.601] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:03.812] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:04.031] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:04.260] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:04.493] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:04.755] [mlr3] Finished benchmark
## INFO [16:29:04.882] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:04.890] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:05.517] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:06.264] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:06.969] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:07.661] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:08.350] [mlr3] Finished benchmark
## INFO [16:29:08.482] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:08.491] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:08.677] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:08.913] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:09.097] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:09.279] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:09.472] [mlr3] Finished benchmark
## INFO [16:29:09.614] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:09.623] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:09.842] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:10.078] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:10.305] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:10.507] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:10.770] [mlr3] Finished benchmark
## INFO [16:29:10.904] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:10.915] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:11.233] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:11.533] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:11.829] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:12.107] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:12.376] [mlr3] Finished benchmark
## INFO [16:29:12.501] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:12.510] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:13.236] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:14.115] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:14.944] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:15.633] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:16.402] [mlr3] Finished benchmark
## INFO [16:29:16.598] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:16.616] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:17.220] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:18.122] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:18.629] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:19.146] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:19.653] [mlr3] Finished benchmark
## INFO [16:29:19.778] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:19.788] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:20.624] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:21.630] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:22.590] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:23.443] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:24.262] [mlr3] Finished benchmark
## INFO [16:29:24.390] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:24.398] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:25.036] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:25.681] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:26.321] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:26.977] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:27.969] [mlr3] Finished benchmark
## INFO [16:29:28.363] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:28.382] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:29.050] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:29.632] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:30.135] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:30.631] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:31.102] [mlr3] Finished benchmark
## INFO [16:29:31.280] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:31.298] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:31.693] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:32.087] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:32.470] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:32.861] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:33.303] [mlr3] Finished benchmark
## INFO [16:29:33.514] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:33.528] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:33.790] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:34.068] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:34.330] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:34.611] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:34.887] [mlr3] Finished benchmark
## INFO [16:29:35.033] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:35.043] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:35.311] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:35.587] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:35.857] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:36.130] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:36.410] [mlr3] Finished benchmark
## INFO [16:29:36.569] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:36.579] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:37.225] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:37.879] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:38.438] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:39.121] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:39.700] [mlr3] Finished benchmark
## INFO [16:29:39.827] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:39.836] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:40.529] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:41.208] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:42.028] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:42.770] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:43.554] [mlr3] Finished benchmark
## INFO [16:29:43.683] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:43.692] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:44.154] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:44.621] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:45.154] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:45.615] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:46.082] [mlr3] Finished benchmark
## INFO [16:29:46.218] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:46.227] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:46.710] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:47.208] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:47.703] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:48.192] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:48.647] [mlr3] Finished benchmark
## INFO [16:29:49.879] [mlr3] Applying learner 'regr.ranger.tuned' on task 'challenge' (iter 1/5)
## INFO [16:29:50.052] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:50.062] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:50.651] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:51.233] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:51.850] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:52.509] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:53.180] [mlr3] Finished benchmark
## INFO [16:29:53.340] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:53.349] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:53.570] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:53.794] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:54.009] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:54.235] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:54.470] [mlr3] Finished benchmark
## INFO [16:29:54.602] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:54.610] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:55.021] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:55.450] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:55.816] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:56.189] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:56.572] [mlr3] Finished benchmark
## INFO [16:29:56.715] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:56.726] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:56.941] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:57.170] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:57.374] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:57.597] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:57.823] [mlr3] Finished benchmark
## INFO [16:29:57.960] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:57.972] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:58.138] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:58.346] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:58.508] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:29:58.673] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:29:58.835] [mlr3] Finished benchmark
## INFO [16:29:58.960] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:29:58.968] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:29:59.236] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:29:59.514] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:29:59.788] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:30:00.074] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:30:00.351] [mlr3] Finished benchmark
## INFO [16:30:00.482] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:30:00.491] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:30:00.874] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:30:01.303] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:30:01.685] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:30:02.127] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:30:02.568] [mlr3] Finished benchmark
## INFO [16:30:02.706] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:30:02.714] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:30:03.305] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:30:03.807] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:30:04.329] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:30:04.843] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:30:05.364] [mlr3] Finished benchmark
## INFO [16:30:05.532] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:30:05.544] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:30:06.089] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:30:06.643] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:30:07.229] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:30:07.811] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:30:08.364] [mlr3] Finished benchmark
## INFO [16:30:08.497] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:30:08.505] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:30:09.029] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:30:09.558] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:30:10.093] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:30:10.660] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:30:11.198] [mlr3] Finished benchmark
## INFO [16:30:11.323] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:30:11.332] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:30:11.708] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:30:12.148] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:30:12.569] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:30:13.008] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:30:13.396] [mlr3] Finished benchmark
## INFO [16:30:13.553] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:30:13.565] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:30:13.980] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:30:14.343] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:30:14.709] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:30:15.082] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:30:15.431] [mlr3] Finished benchmark
## INFO [16:30:15.557] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:30:15.565] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:30:15.795] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:30:16.031] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:30:16.294] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:30:16.543] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:30:16.779] [mlr3] Finished benchmark
## INFO [16:30:16.905] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:30:16.914] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:30:17.899] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:30:18.744] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:30:19.555] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:30:20.653] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:30:21.490] [mlr3] Finished benchmark
## INFO [16:30:21.623] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:30:21.632] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:30:23.138] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:30:24.312] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:30:25.495] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:30:26.715] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:30:28.386] [mlr3] Finished benchmark
## INFO [16:30:28.685] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:30:28.699] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:30:29.158] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:30:29.561] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:30:29.948] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:30:30.256] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:30:30.522] [mlr3] Finished benchmark
## INFO [16:30:30.707] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:30:30.719] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:30:31.515] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:30:32.290] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:30:33.051] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:30:33.869] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:30:34.566] [mlr3] Finished benchmark
## INFO [16:30:34.752] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:30:34.760] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:30:35.481] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:30:36.162] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:30:36.849] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:30:37.617] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:30:38.373] [mlr3] Finished benchmark
## INFO [16:30:38.503] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:30:38.512] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:30:38.787] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:30:39.061] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:30:39.415] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:30:39.674] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:30:39.931] [mlr3] Finished benchmark
## INFO [16:30:40.061] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:30:40.069] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:30:41.041] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:30:42.013] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:30:43.167] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:30:44.236] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:30:45.226] [mlr3] Finished benchmark
## INFO [16:30:46.950] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:31:09.750] [mlr3] Applying learner 'regr.ranger.tuned' on task 'challenge' (iter 5/5)
## INFO [16:31:09.880] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:31:09.890] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:31:10.257] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:31:10.658] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:31:11.008] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:31:11.367] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:31:11.743] [mlr3] Finished benchmark
## INFO [16:31:11.901] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:31:11.911] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:31:12.380] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:31:12.875] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:31:13.380] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:31:14.259] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:31:14.744] [mlr3] Finished benchmark
## INFO [16:31:14.871] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:31:14.879] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:31:15.321] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:31:15.786] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:31:16.229] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:31:16.675] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:31:17.128] [mlr3] Finished benchmark
## INFO [16:31:17.254] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:31:17.263] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:31:17.491] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:31:17.731] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:31:18.007] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:31:18.268] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:31:18.502] [mlr3] Finished benchmark
## INFO [16:31:18.690] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:31:18.698] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:31:19.328] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:31:19.919] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:31:20.495] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:31:21.086] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:31:21.686] [mlr3] Finished benchmark
## INFO [16:31:21.807] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:31:21.817] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:31:22.390] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:31:23.034] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:31:23.660] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:31:24.310] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:31:24.904] [mlr3] Finished benchmark
## INFO [16:31:25.026] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:31:25.035] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:31:25.470] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:31:25.898] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:31:26.331] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:31:26.733] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:31:27.101] [mlr3] Finished benchmark
## INFO [16:31:27.231] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:31:27.240] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:31:27.710] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:31:28.228] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:31:28.844] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:31:29.341] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:31:29.777] [mlr3] Finished benchmark
## INFO [16:31:29.900] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:31:29.909] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:31:31.395] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:31:32.437] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:31:33.740] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:31:35.118] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:31:36.287] [mlr3] Finished benchmark
## INFO [16:31:36.419] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:31:36.427] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:31:37.369] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:31:38.635] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:31:40.227] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:31:41.713] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:31:42.805] [mlr3] Finished benchmark
## INFO [16:31:42.940] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:31:42.948] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:31:43.990] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:31:44.904] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:31:45.737] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:31:46.609] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:31:47.433] [mlr3] Finished benchmark
## INFO [16:31:47.623] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:31:47.631] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:31:47.872] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:31:48.109] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:31:48.378] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:31:48.652] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:31:48.950] [mlr3] Finished benchmark
## INFO [16:31:49.082] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:31:49.092] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:31:49.402] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:31:49.687] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:31:49.966] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:31:50.251] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:31:50.501] [mlr3] Finished benchmark
## INFO [16:31:50.630] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:31:50.638] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:31:50.964] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:31:51.243] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:31:51.507] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:31:51.792] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:31:52.066] [mlr3] Finished benchmark
## INFO [16:31:52.187] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:31:52.197] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:31:52.450] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:31:52.699] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:31:52.950] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:31:53.213] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:31:53.505] [mlr3] Finished benchmark
## INFO [16:31:53.662] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:31:53.671] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:31:54.228] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:31:54.787] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:31:55.290] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:31:55.821] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:31:56.301] [mlr3] Finished benchmark
## INFO [16:31:56.494] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:31:56.502] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:31:57.314] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:31:58.150] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:31:59.169] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:32:00.049] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:32:00.903] [mlr3] Finished benchmark
## INFO [16:32:01.026] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:32:01.035] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:32:01.366] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:32:01.699] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:32:02.039] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:32:02.380] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:32:02.720] [mlr3] Finished benchmark
## INFO [16:32:02.849] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:32:02.904] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:32:03.121] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:32:03.336] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:32:03.569] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:32:03.805] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:32:04.050] [mlr3] Finished benchmark
## INFO [16:32:04.190] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:32:04.202] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:32:05.184] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:32:06.058] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:32:07.052] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:32:07.959] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:32:09.192] [mlr3] Finished benchmark
## INFO [16:32:10.687] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:32:32.641] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:32:56.682] [mlr3] Applying learner 'regr.ranger.tuned' on task 'challenge' (iter 3/5)
## INFO [16:32:56.815] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:32:56.824] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:32:57.759] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:32:58.251] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:32:58.744] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:32:59.332] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:32:59.830] [mlr3] Finished benchmark
## INFO [16:32:59.958] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:32:59.967] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:00.228] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:00.440] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:00.655] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:00.875] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:01.092] [mlr3] Finished benchmark
## INFO [16:33:01.220] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:33:01.229] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:01.727] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:02.231] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:02.743] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:03.259] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:03.789] [mlr3] Finished benchmark
## INFO [16:33:03.977] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:33:03.987] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:04.174] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:04.381] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:04.603] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:04.782] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:04.979] [mlr3] Finished benchmark
## INFO [16:33:05.130] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:33:05.138] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:05.765] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:06.351] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:06.928] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:07.524] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:08.118] [mlr3] Finished benchmark
## INFO [16:33:08.298] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:33:08.305] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:08.644] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:09.002] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:09.419] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:09.845] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:10.207] [mlr3] Finished benchmark
## INFO [16:33:10.332] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:33:10.341] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:10.583] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:10.808] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:11.034] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:11.261] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:11.497] [mlr3] Finished benchmark
## INFO [16:33:11.628] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:33:11.638] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:11.833] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:12.029] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:12.227] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:12.857] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:13.059] [mlr3] Finished benchmark
## INFO [16:33:13.179] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:33:13.190] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:13.870] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:14.616] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:15.349] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:16.105] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:16.767] [mlr3] Finished benchmark
## INFO [16:33:16.895] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:33:16.904] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:17.119] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:17.333] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:17.544] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:17.770] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:17.983] [mlr3] Finished benchmark
## INFO [16:33:18.168] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:33:18.176] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:18.570] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:18.947] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:19.334] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:19.792] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:20.264] [mlr3] Finished benchmark
## INFO [16:33:20.390] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:33:20.401] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:20.703] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:20.994] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:21.308] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:21.577] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:21.887] [mlr3] Finished benchmark
## INFO [16:33:22.034] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:33:22.043] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:22.824] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:23.581] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:24.357] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:25.267] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:26.075] [mlr3] Finished benchmark
## INFO [16:33:26.204] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:33:26.213] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:26.860] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:27.452] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:28.088] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:28.701] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:29.327] [mlr3] Finished benchmark
## INFO [16:33:29.476] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:33:29.486] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:29.961] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:30.343] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:30.769] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:31.157] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:31.549] [mlr3] Finished benchmark
## INFO [16:33:31.737] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:33:31.746] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:32.411] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:33.144] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:33.802] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:34.463] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:35.223] [mlr3] Finished benchmark
## INFO [16:33:35.373] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:33:35.381] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:35.606] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:35.856] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:36.075] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:36.284] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:36.510] [mlr3] Finished benchmark
## INFO [16:33:36.642] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:33:36.650] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:37.094] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:37.590] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:38.019] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:38.460] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:38.904] [mlr3] Finished benchmark
## INFO [16:33:39.031] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:33:39.039] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:39.347] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:39.700] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:40.094] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:40.486] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:40.831] [mlr3] Finished benchmark
## INFO [16:33:40.959] [mlr3] Running benchmark with 5 resampling iterations
## INFO [16:33:40.969] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:33:41.527] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 2/5)
## INFO [16:33:42.036] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 1/5)
## INFO [16:33:42.529] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 5/5)
## INFO [16:33:43.041] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 3/5)
## INFO [16:33:43.551] [mlr3] Finished benchmark
## INFO [16:33:44.676] [mlr3] Applying learner 'regr.ranger' on task 'challenge' (iter 4/5)
## INFO [16:34:14.051] [mlr3] Finished benchmarkTerlihat model hasil tuning dengan sebelum tuning memiliki MAE yang tidak berbeda jauh
Hiperparameter Terbaik
get_param_res <- function(i){
as.data.table(bmrpms)$learner[[i]]$tuning_result
}
best_rf_param =map_dfr(6:10,get_param_res)
best_rf_param %>% slice_min(regr.mae)best_rf_param_value <- c(best_rf_param %>%
slice_min(regr.mae) %>%
pull(mtry),
best_rf_param %>%
slice_min(regr.mae) %>%
pull(max.depth), best_rf_param %>%
slice_min(regr.mae) %>%
pull(num.trees) , best_rf_param %>%
slice_min(regr.mae) %>%
pull(min.node.size),best_rf_param %>%
slice_min(regr.mae) %>%
pull(min.node.size)
)Terlihat diatas adalah masing-masing nilai hiperparameter yang dinyatakan terbaik untuk pemodelan
Memilih Model
Karena model hasil tuning dengan sebelum tuning memiliki MAE yang tidak berbeda jauh, maka kedua model ini akan digunakan untuk memprediksi yang kemudian akan dilihat performanya berdasarkan MAD pada kaggle.
Memprediksi Respon pada Data Baru
Gunakan sintaks berikut untuk memprediksi respon pada data baru
prediksi_rf_best <- model_rf_best$predict_newdata(newdata = test)
prediksi_rf_best=as.data.table(prediksi_rf_best)
prediksi_rf_best=cbind(test1$id,prediksi_rf_best$response)
head(prediksi_rf_best)## [,1] [,2]
## [1,] 17423675 225.15420
## [2,] 6226658 81.12843
## [3,] 3563677 130.41325
## [4,] 17615783 82.88992
## [5,] 2479317 332.57886
## [6,] 14122244 241.42357prediksi_rf <- model_rfpms$predict_newdata(newdata = test)
prediksi_rf=as.data.table(prediksi_rf)
prediksi_rf=cbind(test1$id,prediksi_rf$response)
head(prediksi_rf)## [,1] [,2]
## [1,] 17423675 235.4837
## [2,] 6226658 81.3324
## [3,] 3563677 130.2355
## [4,] 17615783 78.1177
## [5,] 2479317 313.3038
## [6,] 14122244 247.3532Terlihat untuk kolom pertama menyatakan ID dari rumah sedangkan kolom kedua menyatakan prediksi harganya
Pembahasan Hasil Pemodelan
Setelah hasil kedua prediksi tersebut disubmit ke kaggle ternyata model setelah tuning menghasilkan MAE 63.80105 sedangkan model tanpa tuning MAE nya 61.33195. Hal tersebut menandakan model sebelum di tuning lebih baik untuk memprediksi harga dibandingkan model setelah tuning. Selanjutnya, model yang akan digunakan adalah model random forest sebelum dituning. Random forest dapat menghasilkan nilai variable importance atau nilai kepentingan peubah dimana nilai ini menunjukan seberapa penting suatu peubah. Peubah yang memiliki nilai impurity lebih kecil menandakan bahwa peubah tersebut lebih penting. Begitu juga sebaliknya.
## accommodates bathrooms bed_type
## 170883148 192221773 3544779
## bedrooms beds cancellation_policy
## 177042607 87382232 30111966
## city cleaning_fee host_has_profile_pic
## 79262142 21403234 2552582
## host_identity_verified host_response_rate instant_bookable
## 17138754 47138477 15769432
## number_of_reviews review_scores_rating room_type
## 77603362 41639188 106677230importance <- data.frame(Predictors = names(model_rfpms$model$variable.importance),
impurity = model_rfpms$model$variable.importance
)
rownames(importance) <- NULL
importance[order(importance$impurity),]Hasil diatas menunjukan peubah dengan nilai impurity yang besar seperti bathrooms, accommodates, room_type dan bedrooms cenderung dianggap penting sementara untuk peubah dengan nilai impurity yang kecil adalah host_has_profile_pic dan bed_type. Peubah host_has_profile_pic dan bed_type dianggap sebagai peubah yang tidak penting.
Kesimpulan
Model yang digunakan untuk prediksi harga adalah model random forest sebelum di tuning. Model ini memberikan hasil MAE sebesar 61.33195 pada kaggle untuk prediksi data baru. Model ini menghasilkan tingkat kepentingan suatu peubah. Berdasarkan variable importance, Peubah host_has_profile_pic dan bed_type dianggap sebagai peubah yang tidak penting, sementara untuk peubah seperti bathrooms, accommodates, room_type dan bedrooms cenderung dianggap penting.