Classification Boosting Learner
Source:R/learner_adabag_classif_adabag.R
mlr_learners_classif.adabag.RdClassification boosting algorithm.
Calls adabag::boosting() from adabag.
Initial parameter values
xval:Actual default: 10L
Initial value: 0L
Reason for change: Set to 0 for speed.
Parameters
| Id | Type | Default | Levels | Range |
| boos | logical | TRUE | TRUE, FALSE | - |
| coeflearn | character | Breiman | Breiman, Freund, Zhu | - |
| cp | numeric | 0.01 | \([0, 1]\) | |
| maxcompete | integer | 4 | \([0, \infty)\) | |
| maxdepth | integer | 30 | \([1, 30]\) | |
| maxsurrogate | integer | 5 | \([0, \infty)\) | |
| mfinal | integer | 100 | \([1, \infty)\) | |
| minbucket | integer | - | \([1, \infty)\) | |
| minsplit | integer | 20 | \([1, \infty)\) | |
| newmfinal | integer | - | \((-\infty, \infty)\) | |
| surrogatestyle | integer | 0 | \([0, 1]\) | |
| usesurrogate | integer | 2 | \([0, 2]\) | |
| xval | integer | 0 | \([0, \infty)\) |
References
Alfaro, Esteban, Gamez, Matias, García, Noelia (2013). “adabag: An R Package for Classification with Boosting and Bagging.” Journal of Statistical Software, 54(2), 1-35. doi:10.18637/jss.v054.i02 , https://www.jstatsoft.org/index.php/jss/article/view/v054i02.
See also
as.data.table(mlr_learners)for a table of available Learners in the running session (depending on the loaded packages).Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Super classes
mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifAdabag
Methods
Inherited methods
mlr3::Learner$base_learner()mlr3::Learner$configure()mlr3::Learner$encapsulate()mlr3::Learner$format()mlr3::Learner$help()mlr3::Learner$predict()mlr3::Learner$predict_newdata()mlr3::Learner$print()mlr3::Learner$reset()mlr3::Learner$selected_features()mlr3::Learner$train()mlr3::LearnerClassif$predict_newdata_fast()
Examples
# Define the Learner
learner = lrn("classif.adabag", mfinal = 10L)
print(learner)
#>
#> ── <LearnerClassifAdabag> (classif.adabag): Adabag Boosting ────────────────────
#> • Model: -
#> • Parameters: mfinal=10, xval=0
#> • Packages: mlr3, adabag, and rpart
#> • Predict Types: [response] and prob
#> • Feature Types: integer, numeric, and factor
#> • Encapsulation: none (fallback: -)
#> • Properties: importance, missings, multiclass, and twoclass
#> • Other settings: use_weights = 'error', predict_raw = 'FALSE'
# Define a Task
task = tsk("sonar")
# Create train and test set
ids = partition(task)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
print(learner$model)
#> $formula
#> Class ~ .
#> NULL
#>
#> $trees
#> $trees[[1]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 62 M (0.55395683 0.44604317)
#> 2) V52>=0.00935 75 14 M (0.81333333 0.18666667)
#> 4) V13>=0.16265 67 7 M (0.89552239 0.10447761)
#> 8) V15< 0.6129 59 2 M (0.96610169 0.03389831) *
#> 9) V15>=0.6129 8 3 R (0.37500000 0.62500000) *
#> 5) V13< 0.16265 8 1 R (0.12500000 0.87500000) *
#> 3) V52< 0.00935 64 16 R (0.25000000 0.75000000)
#> 6) V51>=0.01245 24 10 M (0.58333333 0.41666667)
#> 12) V21>=0.606 11 1 M (0.90909091 0.09090909) *
#> 13) V21< 0.606 13 4 R (0.30769231 0.69230769) *
#> 7) V51< 0.01245 40 2 R (0.05000000 0.95000000) *
#>
#> $trees[[2]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 52 M (0.62589928 0.37410072)
#> 2) V10>=0.18485 87 14 M (0.83908046 0.16091954)
#> 4) V36< 0.4682 69 3 M (0.95652174 0.04347826) *
#> 5) V36>=0.4682 18 7 R (0.38888889 0.61111111) *
#> 3) V10< 0.18485 52 14 R (0.26923077 0.73076923)
#> 6) V4>=0.0604 10 1 M (0.90000000 0.10000000) *
#> 7) V4< 0.0604 42 5 R (0.11904762 0.88095238)
#> 14) V33< 0.2833 7 3 M (0.57142857 0.42857143) *
#> 15) V33>=0.2833 35 1 R (0.02857143 0.97142857) *
#>
#> $trees[[3]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 68 M (0.51079137 0.48920863)
#> 2) V9>=0.124 83 23 M (0.72289157 0.27710843)
#> 4) V37< 0.3563 50 3 M (0.94000000 0.06000000) *
#> 5) V37>=0.3563 33 13 R (0.39393939 0.60606061)
#> 10) V45>=0.26515 11 0 M (1.00000000 0.00000000) *
#> 11) V45< 0.26515 22 2 R (0.09090909 0.90909091) *
#> 3) V9< 0.124 56 11 R (0.19642857 0.80357143)
#> 6) V44>=0.19215 24 11 R (0.45833333 0.54166667)
#> 12) V31< 0.43 10 0 M (1.00000000 0.00000000) *
#> 13) V31>=0.43 14 1 R (0.07142857 0.92857143) *
#> 7) V44< 0.19215 32 0 R (0.00000000 1.00000000) *
#>
#> $trees[[4]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 67 R (0.48201439 0.51798561)
#> 2) V49>=0.04425 62 14 M (0.77419355 0.22580645)
#> 4) V44>=0.1434 45 3 M (0.93333333 0.06666667) *
#> 5) V44< 0.1434 17 6 R (0.35294118 0.64705882) *
#> 3) V49< 0.04425 77 19 R (0.24675325 0.75324675)
#> 6) V58>=0.00925 10 1 M (0.90000000 0.10000000) *
#> 7) V58< 0.00925 67 10 R (0.14925373 0.85074627)
#> 14) V43>=0.2653 8 2 M (0.75000000 0.25000000) *
#> 15) V43< 0.2653 59 4 R (0.06779661 0.93220339) *
#>
#> $trees[[5]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 50 R (0.35971223 0.64028777)
#> 2) V11>=0.17665 77 34 M (0.55844156 0.44155844)
#> 4) V27>=0.8422 32 1 M (0.96875000 0.03125000) *
#> 5) V27< 0.8422 45 12 R (0.26666667 0.73333333)
#> 10) V49>=0.0583 10 1 M (0.90000000 0.10000000) *
#> 11) V49< 0.0583 35 3 R (0.08571429 0.91428571) *
#> 3) V11< 0.17665 62 7 R (0.11290323 0.88709677)
#> 6) V19>=0.83495 7 1 M (0.85714286 0.14285714) *
#> 7) V19< 0.83495 55 1 R (0.01818182 0.98181818) *
#>
#> $trees[[6]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 63 M (0.54676259 0.45323741)
#> 2) V51>=0.01555 54 6 M (0.88888889 0.11111111) *
#> 3) V51< 0.01555 85 28 R (0.32941176 0.67058824)
#> 6) V10>=0.22885 30 10 M (0.66666667 0.33333333)
#> 12) V1< 0.0404 20 0 M (1.00000000 0.00000000) *
#> 13) V1>=0.0404 10 0 R (0.00000000 1.00000000) *
#> 7) V10< 0.22885 55 8 R (0.14545455 0.85454545)
#> 14) V31< 0.3186 8 3 M (0.62500000 0.37500000) *
#> 15) V31>=0.3186 47 3 R (0.06382979 0.93617021) *
#>
#> $trees[[7]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 59 R (0.42446043 0.57553957)
#> 2) V48>=0.07585 64 16 M (0.75000000 0.25000000)
#> 4) V36< 0.52295 50 4 M (0.92000000 0.08000000) *
#> 5) V36>=0.52295 14 2 R (0.14285714 0.85714286) *
#> 3) V48< 0.07585 75 11 R (0.14666667 0.85333333)
#> 6) V59>=0.0072 12 4 M (0.66666667 0.33333333) *
#> 7) V59< 0.0072 63 3 R (0.04761905 0.95238095) *
#>
#> $trees[[8]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 64 M (0.53956835 0.46043165)
#> 2) V5>=0.03935 98 32 M (0.67346939 0.32653061)
#> 4) V23>=0.785 39 2 M (0.94871795 0.05128205) *
#> 5) V23< 0.785 59 29 R (0.49152542 0.50847458)
#> 10) V25< 0.51305 27 5 M (0.81481481 0.18518519)
#> 20) V10>=0.197 19 0 M (1.00000000 0.00000000) *
#> 21) V10< 0.197 8 3 R (0.37500000 0.62500000) *
#> 11) V25>=0.51305 32 7 R (0.21875000 0.78125000)
#> 22) V55< 0.00685 7 1 M (0.85714286 0.14285714) *
#> 23) V55>=0.00685 25 1 R (0.04000000 0.96000000) *
#> 3) V5< 0.03935 41 9 R (0.21951220 0.78048780)
#> 6) V51>=0.01515 13 4 M (0.69230769 0.30769231) *
#> 7) V51< 0.01515 28 0 R (0.00000000 1.00000000) *
#>
#> $trees[[9]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 50 R (0.35971223 0.64028777)
#> 2) V45>=0.23645 18 1 M (0.94444444 0.05555556) *
#> 3) V45< 0.23645 121 33 R (0.27272727 0.72727273)
#> 6) V35< 0.099 12 1 M (0.91666667 0.08333333) *
#> 7) V35>=0.099 109 22 R (0.20183486 0.79816514)
#> 14) V59>=0.01205 14 4 M (0.71428571 0.28571429) *
#> 15) V59< 0.01205 95 12 R (0.12631579 0.87368421)
#> 30) V28>=0.92715 14 6 M (0.57142857 0.42857143) *
#> 31) V28< 0.92715 81 4 R (0.04938272 0.95061728) *
#>
#> $trees[[10]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 61 R (0.43884892 0.56115108)
#> 2) V52>=0.0233 24 2 M (0.91666667 0.08333333) *
#> 3) V52< 0.0233 115 39 R (0.33913043 0.66086957)
#> 6) V31< 0.467 41 16 M (0.60975610 0.39024390)
#> 12) V44>=0.1193 31 6 M (0.80645161 0.19354839)
#> 24) V30>=0.25285 24 1 M (0.95833333 0.04166667) *
#> 25) V30< 0.25285 7 2 R (0.28571429 0.71428571) *
#> 13) V44< 0.1193 10 0 R (0.00000000 1.00000000) *
#> 7) V31>=0.467 74 14 R (0.18918919 0.81081081)
#> 14) V16< 0.1644 15 5 M (0.66666667 0.33333333) *
#> 15) V16>=0.1644 59 4 R (0.06779661 0.93220339) *
#>
#>
#> $weights
#> [1] 0.6240720 0.6204556 0.9034564 0.5111766 0.6274507 0.6314966 0.6088633
#> [8] 0.6541699 0.6687504 0.7615020
#>
#> $votes
#> [,1] [,2]
#> [1,] 2.5729256 4.0384680
#> [2,] 1.2746255 5.3367681
#> [3,] 2.3719922 4.2394014
#> [4,] 3.1837358 3.4276578
#> [5,] 1.2519522 5.3594414
#> [6,] 2.4057065 4.2056872
#> [7,] 2.1513628 4.4600309
#> [8,] 1.8644320 4.7469617
#> [9,] 1.4302524 5.1811412
#> [10,] 2.5301941 4.0811995
#> [11,] 2.5301941 4.0811995
#> [12,] 1.9091103 4.7022833
#> [13,] 2.4092460 4.2021477
#> [14,] 2.6375263 3.9738674
#> [15,] 1.1653465 5.4460471
#> [16,] 1.2782419 5.3331517
#> [17,] 1.1352487 5.4761450
#> [18,] 2.4354956 4.1758980
#> [19,] 1.1799270 5.4314666
#> [20,] 1.9414290 4.6699646
#> [21,] 0.6314966 5.9798970
#> [22,] 1.8678107 4.7435830
#> [23,] 2.9169107 3.6944830
#> [24,] 1.7626994 4.8486942
#> [25,] 1.9944374 4.6169563
#> [26,] 2.4209152 4.1904785
#> [27,] 1.8567697 4.7546239
#> [28,] 1.1653465 5.4460471
#> [29,] 0.0000000 6.6113936
#> [30,] 1.7968431 4.8145505
#> [31,] 1.8114236 4.7999700
#> [32,] 2.0461297 4.5652640
#> [33,] 1.1426733 5.4687204
#> [34,] 1.9414290 4.6699646
#> [35,] 1.2726787 5.3387150
#> [36,] 1.9268485 4.6845451
#> [37,] 1.2293190 5.3820747
#> [38,] 1.1653465 5.4460471
#> [39,] 2.0833834 4.5280102
#> [40,] 1.4156719 5.1957217
#> [41,] 1.3703653 5.2410283
#> [42,] 0.6541699 5.9572237
#> [43,] 2.0844223 4.5269714
#> [44,] 0.7615020 5.8498916
#> [45,] 1.1799270 5.4314666
#> [46,] 1.1200400 5.4913537
#> [47,] 1.4302524 5.1811412
#> [48,] 0.6204556 5.9909380
#> [49,] 1.2746255 5.3367681
#> [50,] 1.2519522 5.3594414
#> [51,] 1.9061221 4.7052715
#> [52,] 2.8091490 3.8022446
#> [53,] 2.0397439 4.5716497
#> [54,] 1.9097385 4.7016551
#> [55,] 1.2293190 5.3820747
#> [56,] 2.7602261 3.8511675
#> [57,] 1.1200400 5.4913537
#> [58,] 2.1327754 4.4786183
#> [59,] 2.5205850 4.0908086
#> [60,] 1.8073777 4.8040159
#> [61,] 1.9131172 4.6982764
#> [62,] 2.1664896 4.4449040
#> [63,] 0.9034564 5.7079372
#> [64,] 2.0234964 4.5878972
#> [65,] 1.5275284 5.0838652
#> [66,] 1.8980693 4.7133243
#> [67,] 1.3703653 5.2410283
#> [68,] 6.6113936 0.0000000
#> [69,] 5.3257271 1.2856665
#> [70,] 4.8040159 1.8073777
#> [71,] 5.3598709 1.2515227
#> [72,] 6.1002170 0.5111766
#> [73,] 5.0804865 1.5309071
#> [74,] 5.9839429 0.6274507
#> [75,] 4.6836959 1.9276977
#> [76,] 4.6981156 1.9132780
#> [77,] 4.6911205 1.9202731
#> [78,] 5.9839429 0.6274507
#> [79,] 5.1967606 1.4146331
#> [80,] 5.9839429 0.6274507
#> [81,] 6.6113936 0.0000000
#> [82,] 5.9426433 0.6687504
#> [83,] 5.8498916 0.7615020
#> [84,] 5.3111466 1.3002470
#> [85,] 6.6113936 0.0000000
#> [86,] 6.6113936 0.0000000
#> [87,] 5.3337799 1.2776137
#> [88,] 5.3185712 1.2928224
#> [89,] 6.6113936 0.0000000
#> [90,] 5.3598709 1.2515227
#> [91,] 4.8486942 1.7626994
#> [92,] 4.7580027 1.8533910
#> [93,] 5.3820747 1.2293190
#> [94,] 5.3820747 1.2293190
#> [95,] 4.5693098 2.0420838
#> [96,] 5.9426433 0.6687504
#> [97,] 5.9839429 0.6274507
#> [98,] 4.7093174 1.9020762
#> [99,] 5.3297730 1.2816206
#> [100,] 5.0537673 1.5576263
#> [101,] 5.8498916 0.7615020
#> [102,] 4.8412696 1.7701240
#> [103,] 5.3387150 1.2726787
#> [104,] 3.1390575 3.4723362
#> [105,] 3.2714027 3.3399909
#> [106,] 5.3111466 1.3002470
#> [107,] 4.1985313 2.4128623
#> [108,] 4.0892523 2.5221413
#> [109,] 4.7279048 1.8834888
#> [110,] 5.1811412 1.4302524
#> [111,] 3.9296185 2.6817751
#> [112,] 5.9873216 0.6240720
#> [113,] 5.0804865 1.5309071
#> [114,] 4.7510076 1.8603861
#> [115,] 5.3820747 1.2293190
#> [116,] 5.3784583 1.2329353
#> [117,] 5.3820747 1.2293190
#> [118,] 5.0874816 1.5239120
#> [119,] 5.0391868 1.5722068
#> [120,] 4.0611013 2.5502923
#> [121,] 6.6113936 0.0000000
#> [122,] 5.3710337 1.2403599
#> [123,] 4.2776848 2.3337088
#> [124,] 4.6699646 1.9414290
#> [125,] 5.9572237 0.6541699
#> [126,] 5.1957217 1.4156719
#> [127,] 5.9572237 0.6541699
#> [128,] 5.1957217 1.4156719
#> [129,] 6.1002170 0.5111766
#> [130,] 4.1940949 2.4172988
#> [131,] 5.4761450 1.1352487
#> [132,] 5.8498916 0.7615020
#> [133,] 6.6113936 0.0000000
#> [134,] 5.4913537 1.1200400
#> [135,] 6.6113936 0.0000000
#> [136,] 6.6113936 0.0000000
#> [137,] 4.7146430 1.8967507
#> [138,] 4.4523686 2.1590250
#> [139,] 5.4687204 1.1426733
#>
#> $prob
#> [,1] [,2]
#> [1,] 0.38916540 0.61083460
#> [2,] 0.19279226 0.80720774
#> [3,] 0.35877341 0.64122659
#> [4,] 0.48155291 0.51844709
#> [5,] 0.18936283 0.81063717
#> [6,] 0.36387282 0.63612718
#> [7,] 0.32540231 0.67459769
#> [8,] 0.28200287 0.71799713
#> [9,] 0.21633145 0.78366855
#> [10,] 0.38270209 0.61729791
#> [11,] 0.38270209 0.61729791
#> [12,] 0.28876065 0.71123935
#> [13,] 0.36440819 0.63559181
#> [14,] 0.39893651 0.60106349
#> [15,] 0.17626337 0.82373663
#> [16,] 0.19333925 0.80666075
#> [17,] 0.17171095 0.82828905
#> [18,] 0.36837855 0.63162145
#> [19,] 0.17846873 0.82153127
#> [20,] 0.29364898 0.70635102
#> [21,] 0.09551641 0.90448359
#> [22,] 0.28251391 0.71748609
#> [23,] 0.44119452 0.55880548
#> [24,] 0.26661540 0.73338460
#> [25,] 0.30166671 0.69833329
#> [26,] 0.36617320 0.63382680
#> [27,] 0.28084392 0.71915608
#> [28,] 0.17626337 0.82373663
#> [29,] 0.00000000 1.00000000
#> [30,] 0.27177979 0.72822021
#> [31,] 0.27398514 0.72601486
#> [32,] 0.30948538 0.69051462
#> [33,] 0.17283395 0.82716605
#> [34,] 0.29364898 0.70635102
#> [35,] 0.19249779 0.80750221
#> [36,] 0.29144363 0.70855637
#> [37,] 0.18593946 0.81406054
#> [38,] 0.17626337 0.82373663
#> [39,] 0.31512016 0.68487984
#> [40,] 0.21412610 0.78587390
#> [41,] 0.20727330 0.79272670
#> [42,] 0.09894584 0.90105416
#> [43,] 0.31527729 0.68472271
#> [44,] 0.11518026 0.88481974
#> [45,] 0.17846873 0.82153127
#> [46,] 0.16941057 0.83058943
#> [47,] 0.21633145 0.78366855
#> [48,] 0.09384642 0.90615358
#> [49,] 0.19279226 0.80720774
#> [50,] 0.18936283 0.81063717
#> [51,] 0.28830867 0.71169133
#> [52,] 0.42489514 0.57510486
#> [53,] 0.30851951 0.69148049
#> [54,] 0.28885567 0.71114433
#> [55,] 0.18593946 0.81406054
#> [56,] 0.41749535 0.58250465
#> [57,] 0.16941057 0.83058943
#> [58,] 0.32259089 0.67740911
#> [59,] 0.38124867 0.61875133
#> [60,] 0.27337318 0.72662682
#> [61,] 0.28936671 0.71063329
#> [62,] 0.32769031 0.67230969
#> [63,] 0.13665143 0.86334857
#> [64,] 0.30606200 0.69393800
#> [65,] 0.23104485 0.76895515
#> [66,] 0.28709065 0.71290935
#> [67,] 0.20727330 0.79272670
#> [68,] 1.00000000 0.00000000
#> [69,] 0.80553775 0.19446225
#> [70,] 0.72662682 0.27337318
#> [71,] 0.81070213 0.18929787
#> [72,] 0.92268247 0.07731753
#> [73,] 0.76844411 0.23155589
#> [74,] 0.90509554 0.09490446
#> [75,] 0.70842793 0.29157207
#> [76,] 0.71060897 0.28939103
#> [77,] 0.70955093 0.29044907
#> [78,] 0.90509554 0.09490446
#> [79,] 0.78603103 0.21396897
#> [80,] 0.90509554 0.09490446
#> [81,] 1.00000000 0.00000000
#> [82,] 0.89884881 0.10115119
#> [83,] 0.88481974 0.11518026
#> [84,] 0.80333239 0.19666761
#> [85,] 1.00000000 0.00000000
#> [86,] 1.00000000 0.00000000
#> [87,] 0.80675577 0.19324423
#> [88,] 0.80445539 0.19554461
#> [89,] 1.00000000 0.00000000
#> [90,] 0.81070213 0.18929787
#> [91,] 0.73338460 0.26661540
#> [92,] 0.71966713 0.28033287
#> [93,] 0.81406054 0.18593946
#> [94,] 0.81406054 0.18593946
#> [95,] 0.69112658 0.30887342
#> [96,] 0.89884881 0.10115119
#> [97,] 0.90509554 0.09490446
#> [98,] 0.71230328 0.28769672
#> [99,] 0.80614970 0.19385030
#> [100,] 0.76440273 0.23559727
#> [101,] 0.88481974 0.11518026
#> [102,] 0.73226160 0.26773840
#> [103,] 0.80750221 0.19249779
#> [104,] 0.47479513 0.52520487
#> [105,] 0.49481288 0.50518712
#> [106,] 0.80333239 0.19666761
#> [107,] 0.63504482 0.36495518
#> [108,] 0.61851593 0.38148407
#> [109,] 0.71511470 0.28488530
#> [110,] 0.78366855 0.21633145
#> [111,] 0.59437068 0.40562932
#> [112,] 0.90560659 0.09439341
#> [113,] 0.76844411 0.23155589
#> [114,] 0.71860909 0.28139091
#> [115,] 0.81406054 0.18593946
#> [116,] 0.81351355 0.18648645
#> [117,] 0.81406054 0.18593946
#> [118,] 0.76950215 0.23049785
#> [119,] 0.76219737 0.23780263
#> [120,] 0.61425798 0.38574202
#> [121,] 1.00000000 0.00000000
#> [122,] 0.81239055 0.18760945
#> [123,] 0.64701712 0.35298288
#> [124,] 0.70635102 0.29364898
#> [125,] 0.90105416 0.09894584
#> [126,] 0.78587390 0.21412610
#> [127,] 0.90105416 0.09894584
#> [128,] 0.78587390 0.21412610
#> [129,] 0.92268247 0.07731753
#> [130,] 0.63437379 0.36562621
#> [131,] 0.82828905 0.17171095
#> [132,] 0.88481974 0.11518026
#> [133,] 1.00000000 0.00000000
#> [134,] 0.83058943 0.16941057
#> [135,] 1.00000000 0.00000000
#> [136,] 1.00000000 0.00000000
#> [137,] 0.71310880 0.28689120
#> [138,] 0.67343874 0.32656126
#> [139,] 0.82716605 0.17283395
#>
#> $class
#> [1] "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R"
#> [19] "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R"
#> [37] "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R"
#> [55] "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "R" "M" "M" "M" "M" "M"
#> [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
#> [91] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "R" "R" "M" "M" "M"
#> [109] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
#> [127] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
#>
#> $importance
#> V1 V10 V11 V12 V13 V14 V15 V16
#> 2.6375389 7.0958577 2.6800105 0.0000000 1.6589293 0.0000000 0.9623774 2.0462888
#> V17 V18 V19 V2 V20 V21 V22 V23
#> 0.0000000 0.0000000 1.7181116 0.0000000 0.0000000 0.8425651 0.0000000 2.0114000
#> V24 V25 V26 V27 V28 V29 V3 V30
#> 0.0000000 2.1323494 0.0000000 3.6236633 1.3629813 0.0000000 0.0000000 1.1697043
#> V31 V32 V33 V34 V35 V36 V37 V38
#> 5.9249165 0.0000000 0.6682173 0.0000000 2.3142659 4.3077561 3.3551649 0.0000000
#> V39 V4 V40 V41 V42 V43 V44 V45
#> 0.0000000 1.9148065 0.0000000 0.0000000 0.0000000 1.0499897 5.3077142 6.3924771
#> V46 V47 V48 V49 V5 V50 V51 V52
#> 0.0000000 0.0000000 4.7949050 5.0871324 2.4413756 0.0000000 7.5014294 7.4442873
#> V53 V54 V55 V56 V57 V58 V59 V6
#> 0.0000000 0.0000000 1.4965586 0.0000000 0.0000000 1.5705818 3.2408248 0.0000000
#> V60 V7 V8 V9
#> 0.0000000 0.0000000 0.0000000 5.2458194
#>
#> $terms
#> Class ~ V1 + V10 + V11 + V12 + V13 + V14 + V15 + V16 + V17 +
#> V18 + V19 + V2 + V20 + V21 + V22 + V23 + V24 + V25 + V26 +
#> V27 + V28 + V29 + V3 + V30 + V31 + V32 + V33 + V34 + V35 +
#> V36 + V37 + V38 + V39 + V4 + V40 + V41 + V42 + V43 + V44 +
#> V45 + V46 + V47 + V48 + V49 + V5 + V50 + V51 + V52 + V53 +
#> V54 + V55 + V56 + V57 + V58 + V59 + V6 + V60 + V7 + V8 +
#> V9
#> attr(,"variables")
#> list(Class, V1, V10, V11, V12, V13, V14, V15, V16, V17, V18,
#> V19, V2, V20, V21, V22, V23, V24, V25, V26, V27, V28, V29,
#> V3, V30, V31, V32, V33, V34, V35, V36, V37, V38, V39, V4,
#> V40, V41, V42, V43, V44, V45, V46, V47, V48, V49, V5, V50,
#> V51, V52, V53, V54, V55, V56, V57, V58, V59, V6, V60, V7,
#> V8, V9)
#> attr(,"factors")
#> V1 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V2 V20 V21 V22 V23 V24 V25 V26
#> Class 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V10 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V11 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V12 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V13 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V14 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V15 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
#> V16 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
#> V17 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
#> V18 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
#> V19 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
#> V2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
#> V20 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
#> V21 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
#> V22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
#> V23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
#> V24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
#> V25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
#> V26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
#> V27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V35 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V36 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V37 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V38 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V39 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V40 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V42 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V44 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V47 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V48 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V49 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V51 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V52 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V53 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V56 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V57 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V58 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V60 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V27 V28 V29 V3 V30 V31 V32 V33 V34 V35 V36 V37 V38 V39 V4 V40 V41 V42 V43
#> Class 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V27 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V28 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V29 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V3 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V30 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V31 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V32 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
#> V33 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
#> V34 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
#> V35 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
#> V36 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
#> V37 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
#> V38 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
#> V39 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
#> V4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
#> V40 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
#> V41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
#> V42 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
#> V43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
#> V44 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V47 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V48 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V49 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V51 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V52 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V53 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V56 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V57 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V58 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V60 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V44 V45 V46 V47 V48 V49 V5 V50 V51 V52 V53 V54 V55 V56 V57 V58 V59 V6 V60
#> Class 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V35 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V36 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V37 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V38 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V39 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V40 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V42 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V44 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V45 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V46 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V47 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V48 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V49 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V5 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
#> V50 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
#> V51 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
#> V52 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
#> V53 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
#> V54 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
#> V55 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
#> V56 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
#> V57 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
#> V58 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
#> V59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
#> V6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
#> V60 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
#> V7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> V7 V8 V9
#> Class 0 0 0
#> V1 0 0 0
#> V10 0 0 0
#> V11 0 0 0
#> V12 0 0 0
#> V13 0 0 0
#> V14 0 0 0
#> V15 0 0 0
#> V16 0 0 0
#> V17 0 0 0
#> V18 0 0 0
#> V19 0 0 0
#> V2 0 0 0
#> V20 0 0 0
#> V21 0 0 0
#> V22 0 0 0
#> V23 0 0 0
#> V24 0 0 0
#> V25 0 0 0
#> V26 0 0 0
#> V27 0 0 0
#> V28 0 0 0
#> V29 0 0 0
#> V3 0 0 0
#> V30 0 0 0
#> V31 0 0 0
#> V32 0 0 0
#> V33 0 0 0
#> V34 0 0 0
#> V35 0 0 0
#> V36 0 0 0
#> V37 0 0 0
#> V38 0 0 0
#> V39 0 0 0
#> V4 0 0 0
#> V40 0 0 0
#> V41 0 0 0
#> V42 0 0 0
#> V43 0 0 0
#> V44 0 0 0
#> V45 0 0 0
#> V46 0 0 0
#> V47 0 0 0
#> V48 0 0 0
#> V49 0 0 0
#> V5 0 0 0
#> V50 0 0 0
#> V51 0 0 0
#> V52 0 0 0
#> V53 0 0 0
#> V54 0 0 0
#> V55 0 0 0
#> V56 0 0 0
#> V57 0 0 0
#> V58 0 0 0
#> V59 0 0 0
#> V6 0 0 0
#> V60 0 0 0
#> V7 1 0 0
#> V8 0 1 0
#> V9 0 0 1
#> attr(,"term.labels")
#> [1] "V1" "V10" "V11" "V12" "V13" "V14" "V15" "V16" "V17" "V18" "V19" "V2"
#> [13] "V20" "V21" "V22" "V23" "V24" "V25" "V26" "V27" "V28" "V29" "V3" "V30"
#> [25] "V31" "V32" "V33" "V34" "V35" "V36" "V37" "V38" "V39" "V4" "V40" "V41"
#> [37] "V42" "V43" "V44" "V45" "V46" "V47" "V48" "V49" "V5" "V50" "V51" "V52"
#> [49] "V53" "V54" "V55" "V56" "V57" "V58" "V59" "V6" "V60" "V7" "V8" "V9"
#> attr(,"order")
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [39] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> attr(,"intercept")
#> [1] 1
#> attr(,"response")
#> [1] 1
#> attr(,"predvars")
#> list(Class, V1, V10, V11, V12, V13, V14, V15, V16, V17, V18,
#> V19, V2, V20, V21, V22, V23, V24, V25, V26, V27, V28, V29,
#> V3, V30, V31, V32, V33, V34, V35, V36, V37, V38, V39, V4,
#> V40, V41, V42, V43, V44, V45, V46, V47, V48, V49, V5, V50,
#> V51, V52, V53, V54, V55, V56, V57, V58, V59, V6, V60, V7,
#> V8, V9)
#> attr(,"dataClasses")
#> Class V1 V10 V11 V12 V13 V14 V15
#> "factor" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
#> V16 V17 V18 V19 V2 V20 V21 V22
#> "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
#> V23 V24 V25 V26 V27 V28 V29 V3
#> "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
#> V30 V31 V32 V33 V34 V35 V36 V37
#> "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
#> V38 V39 V4 V40 V41 V42 V43 V44
#> "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
#> V45 V46 V47 V48 V49 V5 V50 V51
#> "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
#> V52 V53 V54 V55 V56 V57 V58 V59
#> "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
#> V6 V60 V7 V8 V9
#> "numeric" "numeric" "numeric" "numeric" "numeric"
#>
#> $call
#> adabag::boosting(formula = formula, data = data, mfinal = 10L,
#> control = list(minsplit = 20L, minbucket = 7, cp = 0.01,
#> maxcompete = 4L, maxsurrogate = 5L, usesurrogate = 2L,
#> surrogatestyle = 0L, maxdepth = 30L, xval = 0L))
#>
#> attr(,"vardep.summary")
#> M R
#> 72 67
#> attr(,"class")
#> [1] "boosting"
print(learner$importance())
#> V51 V52 V10 V45 V31 V44 V9 V49
#> 7.5014294 7.4442873 7.0958577 6.3924771 5.9249165 5.3077142 5.2458194 5.0871324
#> V48 V36 V27 V37 V59 V11 V1 V5
#> 4.7949050 4.3077561 3.6236633 3.3551649 3.2408248 2.6800105 2.6375389 2.4413756
#> V35 V25 V16 V23 V4 V19 V13 V58
#> 2.3142659 2.1323494 2.0462888 2.0114000 1.9148065 1.7181116 1.6589293 1.5705818
#> V55 V28 V30 V43 V15 V21 V33 V12
#> 1.4965586 1.3629813 1.1697043 1.0499897 0.9623774 0.8425651 0.6682173 0.0000000
#> V14 V17 V18 V2 V20 V22 V24 V26
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V29 V3 V32 V34 V38 V39 V40 V41
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V42 V46 V47 V50 V53 V54 V56 V57
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V6 V60 V7 V8
#> 0.0000000 0.0000000 0.0000000 0.0000000
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.2463768