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()
LearnerClassifAdabag$new()
Creates a new instance of this R6 class.
Usage
LearnerClassifAdabag$new()LearnerClassifAdabag$importance()
The importance scores are extracted from the model.
Returns
Named numeric().
Examples
# Define the Learner
learner = lrn("classif.adabag", mfinal = 10L)
print(learner)
#>
#> ── <LearnerClassifAdabag> (classif.adabag): Adabag Boosting ────────────────────
#> • Model: -
#> • Parameters: mfinal=10, xval=0
#> • Packages: mlr3, adabag, and rpart
#> • Predict Types: [response] and prob
#> • Feature Types: integer, numeric, and factor
#> • Encapsulation: none (fallback: -)
#> • Properties: importance, missings, multiclass, and twoclass
#> • Other settings: use_weights = 'error', predict_raw = 'FALSE'
# Define a Task
task = tsk("sonar")
# Create train and test set
ids = partition(task)
# Train the learner on the training ids
learner$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 64 R (0.46043165 0.53956835)
#> 2) V51>=0.015 61 16 M (0.73770492 0.26229508)
#> 4) V12>=0.2436 33 0 M (1.00000000 0.00000000) *
#> 5) V12< 0.2436 28 12 R (0.42857143 0.57142857)
#> 10) V45>=0.252 11 0 M (1.00000000 0.00000000) *
#> 11) V45< 0.252 17 1 R (0.05882353 0.94117647) *
#> 3) V51< 0.015 78 19 R (0.24358974 0.75641026)
#> 6) V22>=0.9367 9 0 M (1.00000000 0.00000000) *
#> 7) V22< 0.9367 69 10 R (0.14492754 0.85507246)
#> 14) V4>=0.05215 15 7 M (0.53333333 0.46666667) *
#> 15) V4< 0.05215 54 2 R (0.03703704 0.96296296) *
#>
#> $trees[[2]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 66 M (0.52517986 0.47482014)
#> 2) V48>=0.0846 59 11 M (0.81355932 0.18644068)
#> 4) V37< 0.4299 42 1 M (0.97619048 0.02380952) *
#> 5) V37>=0.4299 17 7 R (0.41176471 0.58823529) *
#> 3) V48< 0.0846 80 25 R (0.31250000 0.68750000)
#> 6) V12>=0.16675 47 23 M (0.51063830 0.48936170)
#> 12) V51>=0.0127 14 1 M (0.92857143 0.07142857) *
#> 13) V51< 0.0127 33 11 R (0.33333333 0.66666667)
#> 26) V8< 0.05955 8 0 M (1.00000000 0.00000000) *
#> 27) V8>=0.05955 25 3 R (0.12000000 0.88000000) *
#> 7) V12< 0.16675 33 1 R (0.03030303 0.96969697) *
#>
#> $trees[[3]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 67 M (0.51798561 0.48201439)
#> 2) V10>=0.12195 104 36 M (0.65384615 0.34615385)
#> 4) V33>=0.2824 81 18 M (0.77777778 0.22222222)
#> 8) V40< 0.60865 72 10 M (0.86111111 0.13888889)
#> 16) V58>=0.0032 57 2 M (0.96491228 0.03508772) *
#> 17) V58< 0.0032 15 7 R (0.46666667 0.53333333) *
#> 9) V40>=0.60865 9 1 R (0.11111111 0.88888889) *
#> 5) V33< 0.2824 23 5 R (0.21739130 0.78260870)
#> 10) V23>=0.86195 7 2 M (0.71428571 0.28571429) *
#> 11) V23< 0.86195 16 0 R (0.00000000 1.00000000) *
#> 3) V10< 0.12195 35 4 R (0.11428571 0.88571429)
#> 6) V3>=0.04195 7 3 M (0.57142857 0.42857143) *
#> 7) V3< 0.04195 28 0 R (0.00000000 1.00000000) *
#>
#> $trees[[4]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 58 M (0.58273381 0.41726619)
#> 2) V10>=0.18425 86 19 M (0.77906977 0.22093023)
#> 4) V30>=0.24555 73 8 M (0.89041096 0.10958904)
#> 8) V15< 0.6112 65 3 M (0.95384615 0.04615385) *
#> 9) V15>=0.6112 8 3 R (0.37500000 0.62500000) *
#> 5) V30< 0.24555 13 2 R (0.15384615 0.84615385) *
#> 3) V10< 0.18425 53 14 R (0.26415094 0.73584906)
#> 6) V20>=0.87415 10 2 M (0.80000000 0.20000000) *
#> 7) V20< 0.87415 43 6 R (0.13953488 0.86046512)
#> 14) V44>=0.3666 8 3 M (0.62500000 0.37500000) *
#> 15) V44< 0.3666 35 1 R (0.02857143 0.97142857) *
#>
#> $trees[[5]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 63 M (0.54676259 0.45323741)
#> 2) V9>=0.1074 103 30 M (0.70873786 0.29126214)
#> 4) V47>=0.06295 82 14 M (0.82926829 0.17073171)
#> 8) V39>=0.16245 70 6 M (0.91428571 0.08571429) *
#> 9) V39< 0.16245 12 4 R (0.33333333 0.66666667) *
#> 5) V47< 0.06295 21 5 R (0.23809524 0.76190476)
#> 10) V60>=0.0085 7 2 M (0.71428571 0.28571429) *
#> 11) V60< 0.0085 14 0 R (0.00000000 1.00000000) *
#> 3) V9< 0.1074 36 3 R (0.08333333 0.91666667) *
#>
#> $trees[[6]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 57 M (0.58992806 0.41007194)
#> 2) V13>=0.25625 74 12 M (0.83783784 0.16216216)
#> 4) V7< 0.2347 67 5 M (0.92537313 0.07462687) *
#> 5) V7>=0.2347 7 0 R (0.00000000 1.00000000) *
#> 3) V13< 0.25625 65 20 R (0.30769231 0.69230769)
#> 6) V48>=0.0766 27 9 M (0.66666667 0.33333333)
#> 12) V12>=0.10375 20 3 M (0.85000000 0.15000000) *
#> 13) V12< 0.10375 7 1 R (0.14285714 0.85714286) *
#> 7) V48< 0.0766 38 2 R (0.05263158 0.94736842) *
#>
#> $trees[[7]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 69 R (0.49640288 0.50359712)
#> 2) V9>=0.11155 95 32 M (0.66315789 0.33684211)
#> 4) V34< 0.692 82 21 M (0.74390244 0.25609756)
#> 8) V32>=0.2487 65 9 M (0.86153846 0.13846154)
#> 16) V10>=0.1453 55 3 M (0.94545455 0.05454545) *
#> 17) V10< 0.1453 10 4 R (0.40000000 0.60000000) *
#> 9) V32< 0.2487 17 5 R (0.29411765 0.70588235) *
#> 5) V34>=0.692 13 2 R (0.15384615 0.84615385) *
#> 3) V9< 0.11155 44 6 R (0.13636364 0.86363636) *
#>
#> $trees[[8]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 62 M (0.55395683 0.44604317)
#> 2) V36< 0.4927 90 26 M (0.71111111 0.28888889)
#> 4) V4>=0.03755 61 6 M (0.90163934 0.09836066)
#> 8) V42>=0.0851 54 1 M (0.98148148 0.01851852) *
#> 9) V42< 0.0851 7 2 R (0.28571429 0.71428571) *
#> 5) V4< 0.03755 29 9 R (0.31034483 0.68965517)
#> 10) V44>=0.1894 8 0 M (1.00000000 0.00000000) *
#> 11) V44< 0.1894 21 1 R (0.04761905 0.95238095) *
#> 3) V36>=0.4927 49 13 R (0.26530612 0.73469388)
#> 6) V28>=0.9055 11 0 M (1.00000000 0.00000000) *
#> 7) V28< 0.9055 38 2 R (0.05263158 0.94736842) *
#>
#> $trees[[9]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 66 M (0.52517986 0.47482014)
#> 2) V51>=0.01305 75 20 M (0.73333333 0.26666667)
#> 4) V11>=0.13035 56 4 M (0.92857143 0.07142857) *
#> 5) V11< 0.13035 19 3 R (0.15789474 0.84210526) *
#> 3) V51< 0.01305 64 18 R (0.28125000 0.71875000)
#> 6) V55< 0.0062 21 7 M (0.66666667 0.33333333)
#> 12) V37< 0.44565 13 1 M (0.92307692 0.07692308) *
#> 13) V37>=0.44565 8 2 R (0.25000000 0.75000000) *
#> 7) V55>=0.0062 43 4 R (0.09302326 0.90697674) *
#>
#> $trees[[10]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 69 M (0.50359712 0.49640288)
#> 2) V45>=0.252 32 2 M (0.93750000 0.06250000) *
#> 3) V45< 0.252 107 40 R (0.37383178 0.62616822)
#> 6) V37< 0.493 69 30 M (0.56521739 0.43478261)
#> 12) V7< 0.1612 54 16 M (0.70370370 0.29629630)
#> 24) V2>=0.0201 35 3 M (0.91428571 0.08571429) *
#> 25) V2< 0.0201 19 6 R (0.31578947 0.68421053) *
#> 13) V7>=0.1612 15 1 R (0.06666667 0.93333333) *
#> 7) V37>=0.493 38 1 R (0.02631579 0.97368421) *
#>
#>
#> $weights
#> [1] 0.8090480 0.7878344 0.5377499 0.8407692 0.7336125 0.7620299 0.5234449
#> [8] 0.8370839 0.7103999 0.6806368
#>
#> $votes
#> [,1] [,2]
#> [1,] 2.9031893 4.3194202
#> [2,] 3.0887986 4.1338108
#> [3,] 2.6765115 4.5460979
#> [4,] 1.9674573 5.2551521
#> [5,] 2.1762792 5.0463302
#> [6,] 0.0000000 7.2226094
#> [7,] 0.7103999 6.5122095
#> [8,] 1.4142493 5.8083601
#> [9,] 0.5377499 6.6848595
#> [10,] 2.3390035 4.8836059
#> [11,] 0.7620299 6.4605796
#> [12,] 1.3748339 5.8477756
#> [13,] 2.3072823 4.9153271
#> [14,] 2.1875671 5.0350423
#> [15,] 2.2060423 5.0165672
#> [16,] 0.8370839 6.3855255
#> [17,] 0.0000000 7.2226094
#> [18,] 1.3910368 5.8315727
#> [19,] 2.1838819 5.0387276
#> [20,] 2.3364115 4.8861979
#> [21,] 2.2517147 4.9708947
#> [22,] 0.8370839 6.3855255
#> [23,] 2.2834359 4.9391736
#> [24,] 1.3255843 5.8970251
#> [25,] 2.0359843 5.1866252
#> [26,] 0.5377499 6.6848595
#> [27,] 1.3910368 5.8315727
#> [28,] 0.8370839 6.3855255
#> [29,] 0.0000000 7.2226094
#> [30,] 2.3304540 4.8921554
#> [31,] 2.1088278 5.1137816
#> [32,] 1.5426605 5.6799490
#> [33,] 2.2834359 4.9391736
#> [34,] 1.2997798 5.9228296
#> [35,] 0.7620299 6.4605796
#> [36,] 0.7103999 6.5122095
#> [37,] 0.7336125 6.4889970
#> [38,] 2.2602642 4.9623452
#> [39,] 0.0000000 7.2226094
#> [40,] 0.5377499 6.6848595
#> [41,] 1.3642140 5.8583954
#> [42,] 0.0000000 7.2226094
#> [43,] 0.7620299 6.4605796
#> [44,] 1.2997798 5.9228296
#> [45,] 0.7620299 6.4605796
#> [46,] 0.0000000 7.2226094
#> [47,] 2.3584899 4.8641195
#> [48,] 0.6806368 6.5419726
#> [49,] 0.7103999 6.5122095
#> [50,] 1.5474839 5.6751256
#> [51,] 0.0000000 7.2226094
#> [52,] 1.3910368 5.8315727
#> [53,] 0.7103999 6.5122095
#> [54,] 0.8407692 6.3818403
#> [55,] 0.0000000 7.2226094
#> [56,] 0.0000000 7.2226094
#> [57,] 1.6027990 5.6198104
#> [58,] 2.9103931 4.3122163
#> [59,] 3.2916959 3.9309135
#> [60,] 2.2814778 4.9411316
#> [61,] 1.2183868 6.0042226
#> [62,] 0.7103999 6.5122095
#> [63,] 1.4440124 5.7785970
#> [64,] 1.2338448 5.9887646
#> [65,] 2.0448918 5.1777177
#> [66,] 2.1368638 5.0857457
#> [67,] 0.7336125 6.4889970
#> [68,] 2.0804104 5.1421990
#> [69,] 0.7336125 6.4889970
#> [70,] 4.2066543 3.0159551
#> [71,] 6.1614146 1.0611948
#> [72,] 5.1479954 2.0746140
#> [73,] 5.3206454 1.9019640
#> [74,] 5.7012034 1.5214060
#> [75,] 5.5447563 1.6778531
#> [76,] 5.1565041 2.0661053
#> [77,] 4.9153271 2.3072823
#> [78,] 3.7866809 3.4359285
#> [79,] 4.4143830 2.8082265
#> [80,] 5.1247829 2.0978265
#> [81,] 4.4176867 2.8049227
#> [82,] 5.8620806 1.3605288
#> [83,] 5.0742462 2.1483632
#> [84,] 7.2226094 0.0000000
#> [85,] 5.2849152 1.9376942
#> [86,] 6.0185277 1.2040817
#> [87,] 6.5419726 0.6806368
#> [88,] 5.9512470 1.2713624
#> [89,] 5.7012034 1.5214060
#> [90,] 6.6991645 0.5234449
#> [91,] 5.1634535 2.0591559
#> [92,] 5.1777585 2.0448509
#> [93,] 5.1777585 2.0448509
#> [94,] 4.8356612 2.3869482
#> [95,] 5.5976911 1.6249183
#> [96,] 5.0742462 2.1483632
#> [97,] 4.3122163 2.9103931
#> [98,] 6.6991645 0.5234449
#> [99,] 5.8583954 1.3642140
#> [100,] 5.8583954 1.3642140
#> [101,] 5.9655521 1.2570574
#> [102,] 5.9887646 1.2338448
#> [103,] 7.2226094 0.0000000
#> [104,] 6.1614146 1.0611948
#> [105,] 5.0493474 2.1732620
#> [106,] 4.9391327 2.2834767
#> [107,] 5.0463302 2.1762792
#> [108,] 5.0205257 2.2020837
#> [109,] 5.7243751 1.4982343
#> [110,] 5.8970251 1.3255843
#> [111,] 5.5976911 1.6249183
#> [112,] 5.7243751 1.4982343
#> [113,] 5.1951747 2.0274348
#> [114,] 5.7329246 1.4896848
#> [115,] 6.6848595 0.5377499
#> [116,] 3.9295678 3.2930416
#> [117,] 4.4033817 2.8192277
#> [118,] 6.4347750 0.7878344
#> [119,] 6.5122095 0.7103999
#> [120,] 5.0879771 2.1346323
#> [121,] 6.4889970 0.7336125
#> [122,] 4.7357084 2.4869011
#> [123,] 7.2226094 0.0000000
#> [124,] 6.6848595 0.5377499
#> [125,] 6.6848595 0.5377499
#> [126,] 6.6848595 0.5377499
#> [127,] 6.4135614 0.8090480
#> [128,] 6.4889970 0.7336125
#> [129,] 7.2226094 0.0000000
#> [130,] 5.1247829 2.0978265
#> [131,] 5.5727923 1.6498171
#> [132,] 6.6848595 0.5377499
#> [133,] 4.8641195 2.3584899
#> [134,] 5.2706102 1.9519993
#> [135,] 5.9512470 1.2713624
#> [136,] 5.9512470 1.2713624
#> [137,] 6.3855255 0.8370839
#> [138,] 5.5764775 1.6461319
#> [139,] 5.9512470 1.2713624
#>
#> $prob
#> [,1] [,2]
#> [1,] 0.40195850 0.59804150
#> [2,] 0.42765688 0.57234312
#> [3,] 0.37057404 0.62942596
#> [4,] 0.27240256 0.72759744
#> [5,] 0.30131481 0.69868519
#> [6,] 0.00000000 1.00000000
#> [7,] 0.09835779 0.90164221
#> [8,] 0.19580864 0.80419136
#> [9,] 0.07445369 0.92554631
#> [10,] 0.32384466 0.67615534
#> [11,] 0.10550617 0.89449383
#> [12,] 0.19035141 0.80964859
#> [13,] 0.31945273 0.68054727
#> [14,] 0.30287767 0.69712233
#> [15,] 0.30543563 0.69456437
#> [16,] 0.11589772 0.88410228
#> [17,] 0.00000000 1.00000000
#> [18,] 0.19259477 0.80740523
#> [19,] 0.30236743 0.69763257
#> [20,] 0.32348579 0.67651421
#> [21,] 0.31175917 0.68824083
#> [22,] 0.11589772 0.88410228
#> [23,] 0.31615109 0.68384891
#> [24,] 0.18353261 0.81646739
#> [25,] 0.28189040 0.71810960
#> [26,] 0.07445369 0.92554631
#> [27,] 0.19259477 0.80740523
#> [28,] 0.11589772 0.88410228
#> [29,] 0.00000000 1.00000000
#> [30,] 0.32266095 0.67733905
#> [31,] 0.29197589 0.70802411
#> [32,] 0.21358769 0.78641231
#> [33,] 0.31615109 0.68384891
#> [34,] 0.17995986 0.82004014
#> [35,] 0.10550617 0.89449383
#> [36,] 0.09835779 0.90164221
#> [37,] 0.10157167 0.89842833
#> [38,] 0.31294288 0.68705712
#> [39,] 0.00000000 1.00000000
#> [40,] 0.07445369 0.92554631
#> [41,] 0.18888105 0.81111895
#> [42,] 0.00000000 1.00000000
#> [43,] 0.10550617 0.89449383
#> [44,] 0.17995986 0.82004014
#> [45,] 0.10550617 0.89449383
#> [46,] 0.00000000 1.00000000
#> [47,] 0.32654264 0.67345736
#> [48,] 0.09423697 0.90576303
#> [49,] 0.09835779 0.90164221
#> [50,] 0.21425551 0.78574449
#> [51,] 0.00000000 1.00000000
#> [52,] 0.19259477 0.80740523
#> [53,] 0.09835779 0.90164221
#> [54,] 0.11640795 0.88359205
#> [55,] 0.00000000 1.00000000
#> [56,] 0.00000000 1.00000000
#> [57,] 0.22191412 0.77808588
#> [58,] 0.40295590 0.59704410
#> [59,] 0.45574885 0.54425115
#> [60,] 0.31587999 0.68412001
#> [61,] 0.16869066 0.83130934
#> [62,] 0.09835779 0.90164221
#> [63,] 0.19992946 0.80007054
#> [64,] 0.17083089 0.82916911
#> [65,] 0.28312368 0.71687632
#> [66,] 0.29585758 0.70414242
#> [67,] 0.10157167 0.89842833
#> [68,] 0.28804138 0.71195862
#> [69,] 0.10157167 0.89842833
#> [70,] 0.58242860 0.41757140
#> [71,] 0.85307321 0.14692679
#> [72,] 0.71276116 0.28723884
#> [73,] 0.73666526 0.26333474
#> [74,] 0.78935508 0.21064492
#> [75,] 0.76769433 0.23230567
#> [76,] 0.71393921 0.28606079
#> [77,] 0.68054727 0.31945273
#> [78,] 0.52428155 0.47571845
#> [79,] 0.61118949 0.38881051
#> [80,] 0.70954729 0.29045271
#> [81,] 0.61164691 0.38835309
#> [82,] 0.81162919 0.18837081
#> [83,] 0.70255027 0.29744973
#> [84,] 1.00000000 0.00000000
#> [85,] 0.73171826 0.26828174
#> [86,] 0.83328993 0.16671007
#> [87,] 0.90576303 0.09423697
#> [88,] 0.82397464 0.17602536
#> [89,] 0.78935508 0.21064492
#> [90,] 0.92752690 0.07247310
#> [91,] 0.71490138 0.28509862
#> [92,] 0.71688198 0.28311802
#> [93,] 0.71688198 0.28311802
#> [94,] 0.66951720 0.33048280
#> [95,] 0.77502337 0.22497663
#> [96,] 0.70255027 0.29744973
#> [97,] 0.59704410 0.40295590
#> [98,] 0.92752690 0.07247310
#> [99,] 0.81111895 0.18888105
#> [100,] 0.81111895 0.18888105
#> [101,] 0.82595524 0.17404476
#> [102,] 0.82916911 0.17083089
#> [103,] 1.00000000 0.00000000
#> [104,] 0.85307321 0.14692679
#> [105,] 0.69910293 0.30089707
#> [106,] 0.68384325 0.31615675
#> [107,] 0.69868519 0.30131481
#> [108,] 0.69511245 0.30488755
#> [109,] 0.79256329 0.20743671
#> [110,] 0.81646739 0.18353261
#> [111,] 0.77502337 0.22497663
#> [112,] 0.79256329 0.20743671
#> [113,] 0.71929331 0.28070669
#> [114,] 0.79374700 0.20625300
#> [115,] 0.92554631 0.07445369
#> [116,] 0.54406483 0.45593517
#> [117,] 0.60966632 0.39033368
#> [118,] 0.89092109 0.10907891
#> [119,] 0.90164221 0.09835779
#> [120,] 0.70445137 0.29554863
#> [121,] 0.89842833 0.10157167
#> [122,] 0.65567831 0.34432169
#> [123,] 1.00000000 0.00000000
#> [124,] 0.92554631 0.07445369
#> [125,] 0.92554631 0.07445369
#> [126,] 0.92554631 0.07445369
#> [127,] 0.88798398 0.11201602
#> [128,] 0.89842833 0.10157167
#> [129,] 1.00000000 0.00000000
#> [130,] 0.70954729 0.29045271
#> [131,] 0.77157603 0.22842397
#> [132,] 0.92554631 0.07445369
#> [133,] 0.67345736 0.32654264
#> [134,] 0.72973767 0.27026233
#> [135,] 0.82397464 0.17602536
#> [136,] 0.82397464 0.17602536
#> [137,] 0.88410228 0.11589772
#> [138,] 0.77208626 0.22791374
#> [139,] 0.82397464 0.17602536
#>
#> $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" "R" "R" "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" "M" "M" "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
#> 0.0000000 7.2322468 3.4014393 5.3992150 4.2114096 0.0000000 1.1402985 0.0000000
#> V17 V18 V19 V2 V20 V21 V22 V23
#> 0.0000000 0.0000000 0.0000000 1.7060871 1.6908463 0.0000000 2.6761603 0.7591883
#> V24 V25 V26 V27 V28 V29 V3 V30
#> 0.0000000 0.0000000 0.0000000 0.0000000 3.6418497 0.0000000 0.5587626 2.8602333
#> V31 V32 V33 V34 V35 V36 V37 V38
#> 0.0000000 1.2905205 1.7189774 1.1620544 0.0000000 2.9992844 5.3840930 0.0000000
#> V39 V4 V40 V41 V42 V43 V44 V45
#> 1.4412795 4.5981785 1.3750798 0.0000000 1.4269011 0.0000000 3.6060756 5.7468161
#> V46 V47 V48 V49 V5 V50 V51 V52
#> 0.0000000 2.4357174 6.3936663 0.0000000 0.0000000 0.0000000 8.2503944 0.0000000
#> V53 V54 V55 V56 V57 V58 V59 V6
#> 0.0000000 0.0000000 1.8742595 0.0000000 0.0000000 0.9008157 0.0000000 0.0000000
#> V60 V7 V8 V9
#> 0.9925501 4.1926110 2.1011223 6.8318664
#>
#> $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"
#>
#> $call
#> adabag::boosting(formula = formula, data = data, mfinal = 10L,
#> control = list(minsplit = 20L, minbucket = 7, cp = 0.01,
#> maxcompete = 4L, maxsurrogate = 5L, usesurrogate = 2L,
#> surrogatestyle = 0L, maxdepth = 30L, xval = 0L))
#>
#> attr(,"vardep.summary")
#> M R
#> 70 69
#> attr(,"class")
#> [1] "boosting"
print(learner$importance())
#> V51 V10 V9 V48 V45 V12 V37 V4
#> 8.2503944 7.2322468 6.8318664 6.3936663 5.7468161 5.3992150 5.3840930 4.5981785
#> V13 V7 V28 V44 V11 V36 V30 V22
#> 4.2114096 4.1926110 3.6418497 3.6060756 3.4014393 2.9992844 2.8602333 2.6761603
#> V47 V8 V55 V33 V2 V20 V39 V42
#> 2.4357174 2.1011223 1.8742595 1.7189774 1.7060871 1.6908463 1.4412795 1.4269011
#> V40 V32 V34 V15 V60 V58 V23 V3
#> 1.3750798 1.2905205 1.1620544 1.1402985 0.9925501 0.9008157 0.7591883 0.5587626
#> V1 V14 V16 V17 V18 V19 V21 V24
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V25 V26 V27 V29 V31 V35 V38 V41
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V43 V46 V49 V5 V50 V52 V53 V54
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V56 V57 V59 V6
#> 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
#> 0.2753623