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 61 M (0.56115108 0.43884892)
#> 2) V11>=0.2148 80 14 M (0.82500000 0.17500000)
#> 4) V6< 0.1809 72 7 M (0.90277778 0.09722222) *
#> 5) V6>=0.1809 8 1 R (0.12500000 0.87500000) *
#> 3) V11< 0.2148 59 12 R (0.20338983 0.79661017)
#> 6) V31< 0.4362 21 10 M (0.52380952 0.47619048)
#> 12) V23>=0.8046 7 0 M (1.00000000 0.00000000) *
#> 13) V23< 0.8046 14 4 R (0.28571429 0.71428571) *
#> 7) V31>=0.4362 38 1 R (0.02631579 0.97368421) *
#>
#> $trees[[2]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 66 M (0.52517986 0.47482014)
#> 2) V12>=0.1469 105 35 M (0.66666667 0.33333333)
#> 4) V51>=0.01215 67 10 M (0.85074627 0.14925373)
#> 8) V25>=0.4685 54 3 M (0.94444444 0.05555556) *
#> 9) V25< 0.4685 13 6 R (0.46153846 0.53846154) *
#> 5) V51< 0.01215 38 13 R (0.34210526 0.65789474)
#> 10) V2< 0.02095 13 2 M (0.84615385 0.15384615) *
#> 11) V2>=0.02095 25 2 R (0.08000000 0.92000000) *
#> 3) V12< 0.1469 34 3 R (0.08823529 0.91176471) *
#>
#> $trees[[3]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 58 M (0.58273381 0.41726619)
#> 2) V12>=0.22505 88 20 M (0.77272727 0.22727273)
#> 4) V48>=0.04615 74 10 M (0.86486486 0.13513514)
#> 8) V19< 0.8773 65 4 M (0.93846154 0.06153846) *
#> 9) V19>=0.8773 9 3 R (0.33333333 0.66666667) *
#> 5) V48< 0.04615 14 4 R (0.28571429 0.71428571) *
#> 3) V12< 0.22505 51 13 R (0.25490196 0.74509804)
#> 6) V31< 0.34415 19 6 M (0.68421053 0.31578947) *
#> 7) V31>=0.34415 32 0 R (0.00000000 1.00000000) *
#>
#> $trees[[4]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 57 M (0.58992806 0.41007194)
#> 2) V10>=0.16315 88 19 M (0.78409091 0.21590909)
#> 4) V39>=0.0814 81 12 M (0.85185185 0.14814815)
#> 8) V58>=0.00495 52 1 M (0.98076923 0.01923077) *
#> 9) V58< 0.00495 29 11 M (0.62068966 0.37931034)
#> 18) V60< 0.00435 15 0 M (1.00000000 0.00000000) *
#> 19) V60>=0.00435 14 3 R (0.21428571 0.78571429) *
#> 5) V39< 0.0814 7 0 R (0.00000000 1.00000000) *
#> 3) V10< 0.16315 51 13 R (0.25490196 0.74509804)
#> 6) V31< 0.3755 21 8 M (0.61904762 0.38095238)
#> 12) V12>=0.1671 11 0 M (1.00000000 0.00000000) *
#> 13) V12< 0.1671 10 2 R (0.20000000 0.80000000) *
#> 7) V31>=0.3755 30 0 R (0.00000000 1.00000000) *
#>
#> $trees[[5]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 58 R (0.41726619 0.58273381)
#> 2) V11>=0.21705 76 27 M (0.64473684 0.35526316)
#> 4) V51>=0.01375 44 6 M (0.86363636 0.13636364)
#> 8) V34>=0.11155 37 0 M (1.00000000 0.00000000) *
#> 9) V34< 0.11155 7 1 R (0.14285714 0.85714286) *
#> 5) V51< 0.01375 32 11 R (0.34375000 0.65625000)
#> 10) V16< 0.2119 7 0 M (1.00000000 0.00000000) *
#> 11) V16>=0.2119 25 4 R (0.16000000 0.84000000)
#> 22) V35< 0.3663 7 3 M (0.57142857 0.42857143) *
#> 23) V35>=0.3663 18 0 R (0.00000000 1.00000000) *
#> 3) V11< 0.21705 63 9 R (0.14285714 0.85714286)
#> 6) V47>=0.16355 8 1 M (0.87500000 0.12500000) *
#> 7) V47< 0.16355 55 2 R (0.03636364 0.96363636) *
#>
#> $trees[[6]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 64 M (0.53956835 0.46043165)
#> 2) V36< 0.40615 71 16 M (0.77464789 0.22535211)
#> 4) V9>=0.10995 63 8 M (0.87301587 0.12698413)
#> 8) V47>=0.05595 56 2 M (0.96428571 0.03571429) *
#> 9) V47< 0.05595 7 1 R (0.14285714 0.85714286) *
#> 5) V9< 0.10995 8 0 R (0.00000000 1.00000000) *
#> 3) V36>=0.40615 68 20 R (0.29411765 0.70588235)
#> 6) V45>=0.26515 17 2 M (0.88235294 0.11764706) *
#> 7) V45< 0.26515 51 5 R (0.09803922 0.90196078) *
#>
#> $trees[[7]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 69 R (0.49640288 0.50359712)
#> 2) V5>=0.0394 106 41 M (0.61320755 0.38679245)
#> 4) V37< 0.47995 66 13 M (0.80303030 0.19696970)
#> 8) V42>=0.0851 54 3 M (0.94444444 0.05555556) *
#> 9) V42< 0.0851 12 2 R (0.16666667 0.83333333) *
#> 5) V37>=0.47995 40 12 R (0.30000000 0.70000000)
#> 10) V36>=0.75575 15 3 M (0.80000000 0.20000000) *
#> 11) V36< 0.75575 25 0 R (0.00000000 1.00000000) *
#> 3) V5< 0.0394 33 4 R (0.12121212 0.87878788)
#> 6) V14< 0.1101 7 3 M (0.57142857 0.42857143) *
#> 7) V14>=0.1101 26 0 R (0.00000000 1.00000000) *
#>
#> $trees[[8]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 57 R (0.41007194 0.58992806)
#> 2) V54>=0.02075 21 0 M (1.00000000 0.00000000) *
#> 3) V54< 0.02075 118 36 R (0.30508475 0.69491525)
#> 6) V51>=0.01625 45 18 M (0.60000000 0.40000000)
#> 12) V35< 0.669 30 4 M (0.86666667 0.13333333) *
#> 13) V35>=0.669 15 1 R (0.06666667 0.93333333) *
#> 7) V51< 0.01625 73 9 R (0.12328767 0.87671233)
#> 14) V59< 0.0027 8 2 M (0.75000000 0.25000000) *
#> 15) V59>=0.0027 65 3 R (0.04615385 0.95384615) *
#>
#> $trees[[9]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 65 R (0.46762590 0.53237410)
#> 2) V4>=0.03755 80 22 M (0.72500000 0.27500000)
#> 4) V12>=0.1677 62 7 M (0.88709677 0.11290323)
#> 8) V50>=0.0126 51 0 M (1.00000000 0.00000000) *
#> 9) V50< 0.0126 11 4 R (0.36363636 0.63636364) *
#> 5) V12< 0.1677 18 3 R (0.16666667 0.83333333) *
#> 3) V4< 0.03755 59 7 R (0.11864407 0.88135593)
#> 6) V45>=0.3586 7 2 M (0.71428571 0.28571429) *
#> 7) V45< 0.3586 52 2 R (0.03846154 0.96153846) *
#>
#> $trees[[10]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 60 M (0.56834532 0.43165468)
#> 2) V21>=0.58055 74 14 M (0.81081081 0.18918919)
#> 4) V40>=0.1373 66 8 M (0.87878788 0.12121212)
#> 8) V21< 0.97905 57 2 M (0.96491228 0.03508772) *
#> 9) V21>=0.97905 9 3 R (0.33333333 0.66666667) *
#> 5) V40< 0.1373 8 2 R (0.25000000 0.75000000) *
#> 3) V21< 0.58055 65 19 R (0.29230769 0.70769231)
#> 6) V12>=0.2438 26 10 M (0.61538462 0.38461538)
#> 12) V12< 0.35845 17 1 M (0.94117647 0.05882353) *
#> 13) V12>=0.35845 9 0 R (0.00000000 1.00000000) *
#> 7) V12< 0.2438 39 3 R (0.07692308 0.92307692) *
#>
#>
#> $weights
#> [1] 0.7834391 0.6885190 0.6037182 0.8622371 0.9915472 0.9089388 0.8186523
#> [8] 0.6861218 0.6013833 0.7351953
#>
#> $votes
#> [,1] [,2]
#> [1,] 2.9731267 4.7066257
#> [2,] 1.5952655 6.0844869
#> [3,] 2.5063712 5.1733812
#> [4,] 2.2790524 5.4006999
#> [5,] 1.6020915 6.0776609
#> [6,] 2.2835542 5.3961981
#> [7,] 3.2462255 4.4335268
#> [8,] 1.4223705 6.2573818
#> [9,] 0.0000000 7.6797523
#> [10,] 2.0732792 5.6064731
#> [11,] 2.0673387 5.6124137
#> [12,] 2.3763697 5.3033826
#> [13,] 1.9783591 5.7013932
#> [14,] 0.0000000 7.6797523
#> [15,] 1.8537843 5.8259680
#> [16,] 2.1988157 5.4809366
#> [17,] 3.0680468 4.6117056
#> [18,] 0.8186523 6.8611000
#> [19,] 1.7275911 5.9521612
#> [20,] 1.3871574 6.2925949
#> [21,] 2.3763697 5.3033826
#> [22,] 1.2051016 6.4746507
#> [23,] 0.8186523 6.8611000
#> [24,] 1.4213171 6.2584352
#> [25,] 2.7112195 4.9685328
#> [26,] 0.7351953 6.9445571
#> [27,] 0.8186523 6.8611000
#> [28,] 0.8186523 6.8611000
#> [29,] 1.5483589 6.1313934
#> [30,] 0.6885190 6.9912333
#> [31,] 0.0000000 7.6797523
#> [32,] 1.2922373 6.3875150
#> [33,] 2.5018694 5.1778829
#> [34,] 2.1988157 5.4809366
#> [35,] 1.6456762 6.0340761
#> [36,] 2.2478523 5.4319000
#> [37,] 2.0274325 5.6523198
#> [38,] 1.3389135 6.3408388
#> [39,] 0.0000000 7.6797523
#> [40,] 1.5071714 6.1725810
#> [41,] 0.6885190 6.9912333
#> [42,] 1.4213171 6.2584352
#> [43,] 1.4213171 6.2584352
#> [44,] 0.8622371 6.8175153
#> [45,] 1.4213171 6.2584352
#> [46,] 1.5071714 6.1725810
#> [47,] 2.1575658 5.5221865
#> [48,] 0.9089388 6.7708135
#> [49,] 0.6861218 6.9936305
#> [50,] 2.1084924 5.5712599
#> [51,] 1.4223705 6.2573818
#> [52,] 1.4223705 6.2573818
#> [53,] 2.2047563 5.4749961
#> [54,] 0.9089388 6.7708135
#> [55,] 1.3871574 6.2925949
#> [56,] 2.5101816 5.1695707
#> [57,] 2.6839252 4.9958271
#> [58,] 1.4695610 6.2101913
#> [59,] 2.4635054 5.2162469
#> [60,] 2.3317981 5.3479543
#> [61,] 1.5538476 6.1259048
#> [62,] 1.5974579 6.0822945
#> [63,] 1.5186344 6.1611179
#> [64,] 1.7275911 5.9521612
#> [65,] 0.0000000 7.6797523
#> [66,] 5.9873744 1.6923780
#> [67,] 6.2584352 1.4213171
#> [68,] 5.4428744 2.2368780
#> [69,] 5.6546547 2.0250977
#> [70,] 6.1725810 1.5071714
#> [71,] 5.7013932 1.9783591
#> [72,] 4.8795901 2.8001623
#> [73,] 5.0907472 2.5890051
#> [74,] 5.0931444 2.5866079
#> [75,] 5.1778829 2.5018694
#> [76,] 5.5573997 2.1223527
#> [77,] 6.1694302 1.5103222
#> [78,] 5.9530098 1.7267425
#> [79,] 6.6882051 0.9915472
#> [80,] 6.0020832 1.6776691
#> [81,] 6.0020832 1.6776691
#> [82,] 6.0844869 1.5952655
#> [83,] 7.6797523 0.0000000
#> [84,] 6.4746507 1.2051016
#> [85,] 6.3898499 1.2899024
#> [86,] 6.2573818 1.4223705
#> [87,] 7.0760341 0.6037182
#> [88,] 4.5636600 3.1160923
#> [89,] 5.4373857 2.2423666
#> [90,] 6.9912333 0.6885190
#> [91,] 6.3898499 1.2899024
#> [92,] 6.3051114 1.3746409
#> [93,] 5.9521612 1.7275911
#> [94,] 6.8963132 0.7834391
#> [95,] 6.8611000 0.8186523
#> [96,] 5.3479543 2.3317981
#> [97,] 6.1289962 1.5507561
#> [98,] 7.6797523 0.0000000
#> [99,] 5.1778829 2.5018694
#> [100,] 5.6546547 2.0250977
#> [101,] 4.9698766 2.7098757
#> [102,] 5.3983027 2.2814496
#> [103,] 6.8611000 0.8186523
#> [104,] 6.9912333 0.6885190
#> [105,] 6.9936305 0.6861218
#> [106,] 6.8611000 0.8186523
#> [107,] 5.7394555 1.9402968
#> [108,] 6.0868217 1.5929306
#> [109,] 6.9936305 0.6861218
#> [110,] 6.7708135 0.9089388
#> [111,] 4.2674973 3.4122550
#> [112,] 5.3455571 2.3341953
#> [113,] 4.7969817 2.8827707
#> [114,] 6.3899122 1.2898401
#> [115,] 5.2660393 2.4137130
#> [116,] 6.3922471 1.2875052
#> [117,] 6.1749782 1.5047742
#> [118,] 6.9936305 0.6861218
#> [119,] 4.8795901 2.8001623
#> [120,] 5.7037281 1.9760242
#> [121,] 6.8963132 0.7834391
#> [122,] 7.6797523 0.0000000
#> [123,] 7.6797523 0.0000000
#> [124,] 7.6797523 0.0000000
#> [125,] 6.2584352 1.4213171
#> [126,] 6.9936305 0.6861218
#> [127,] 6.2597167 1.4200357
#> [128,] 7.0783690 0.6013833
#> [129,] 7.0783690 0.6013833
#> [130,] 7.0783690 0.6013833
#> [131,] 6.0823200 1.5974323
#> [132,] 6.3431737 1.3365786
#> [133,] 6.0823200 1.5974323
#> [134,] 6.2597167 1.4200357
#> [135,] 6.2161319 1.4636204
#> [136,] 6.9445571 0.7351953
#> [137,] 6.1749782 1.5047742
#> [138,] 7.0783690 0.6013833
#> [139,] 5.6124137 2.0673387
#>
#> $prob
#> [,1] [,2]
#> [1,] 0.38713835 0.61286165
#> [2,] 0.20772356 0.79227644
#> [3,] 0.32636094 0.67363906
#> [4,] 0.29676119 0.70323881
#> [5,] 0.20861239 0.79138761
#> [6,] 0.29734737 0.70265263
#> [7,] 0.42269925 0.57730075
#> [8,] 0.18521047 0.81478953
#> [9,] 0.00000000 1.00000000
#> [10,] 0.26996694 0.73003306
#> [11,] 0.26919340 0.73080660
#> [12,] 0.30943312 0.69056688
#> [13,] 0.25760715 0.74239285
#> [14,] 0.00000000 1.00000000
#> [15,] 0.24138595 0.75861405
#> [16,] 0.28631336 0.71368664
#> [17,] 0.39949814 0.60050186
#> [18,] 0.10659879 0.89340121
#> [19,] 0.22495402 0.77504598
#> [20,] 0.18062528 0.81937472
#> [21,] 0.30943312 0.69056688
#> [22,] 0.15691933 0.84308067
#> [23,] 0.10659879 0.89340121
#> [24,] 0.18507330 0.81492670
#> [25,] 0.35303476 0.64696524
#> [26,] 0.09573164 0.90426836
#> [27,] 0.10659879 0.89340121
#> [28,] 0.10659879 0.89340121
#> [29,] 0.20161574 0.79838426
#> [30,] 0.08965381 0.91034619
#> [31,] 0.00000000 1.00000000
#> [32,] 0.16826549 0.83173451
#> [33,] 0.32577475 0.67422525
#> [34,] 0.28631336 0.71368664
#> [35,] 0.21428767 0.78571233
#> [36,] 0.29269854 0.70730146
#> [37,] 0.26399713 0.73600287
#> [38,] 0.17434332 0.82565668
#> [39,] 0.00000000 1.00000000
#> [40,] 0.19625260 0.80374740
#> [41,] 0.08965381 0.91034619
#> [42,] 0.18507330 0.81492670
#> [43,] 0.18507330 0.81492670
#> [44,] 0.11227407 0.88772593
#> [45,] 0.18507330 0.81492670
#> [46,] 0.19625260 0.80374740
#> [47,] 0.28094211 0.71905789
#> [48,] 0.11835523 0.88164477
#> [49,] 0.08934166 0.91065834
#> [50,] 0.27455214 0.72544786
#> [51,] 0.18521047 0.81478953
#> [52,] 0.18521047 0.81478953
#> [53,] 0.28708690 0.71291310
#> [54,] 0.11835523 0.88164477
#> [55,] 0.18062528 0.81937472
#> [56,] 0.32685711 0.67314289
#> [57,] 0.34948070 0.65051930
#> [58,] 0.19135526 0.80864474
#> [59,] 0.32077928 0.67922072
#> [60,] 0.30362933 0.69637067
#> [61,] 0.20233043 0.79766957
#> [62,] 0.20800903 0.79199097
#> [63,] 0.19774523 0.80225477
#> [64,] 0.22495402 0.77504598
#> [65,] 0.00000000 1.00000000
#> [66,] 0.77963118 0.22036882
#> [67,] 0.81492670 0.18507330
#> [68,] 0.70873045 0.29126955
#> [69,] 0.73630691 0.26369309
#> [70,] 0.80374740 0.19625260
#> [71,] 0.74239285 0.25760715
#> [72,] 0.63538378 0.36461622
#> [73,] 0.66287909 0.33712091
#> [74,] 0.66319123 0.33680877
#> [75,] 0.67422525 0.32577475
#> [76,] 0.72364309 0.27635691
#> [77,] 0.80333713 0.19666287
#> [78,] 0.77515649 0.22484351
#> [79,] 0.87088812 0.12911188
#> [80,] 0.78154646 0.21845354
#> [81,] 0.78154646 0.21845354
#> [82,] 0.79227644 0.20772356
#> [83,] 1.00000000 0.00000000
#> [84,] 0.84308067 0.15691933
#> [85,] 0.83203854 0.16796146
#> [86,] 0.81478953 0.18521047
#> [87,] 0.92138832 0.07861168
#> [88,] 0.59424573 0.40575427
#> [89,] 0.70801576 0.29198424
#> [90,] 0.91034619 0.08965381
#> [91,] 0.83203854 0.16796146
#> [92,] 0.82100453 0.17899547
#> [93,] 0.77504598 0.22495402
#> [94,] 0.89798640 0.10201360
#> [95,] 0.89340121 0.10659879
#> [96,] 0.69637067 0.30362933
#> [97,] 0.79807212 0.20192788
#> [98,] 1.00000000 0.00000000
#> [99,] 0.67422525 0.32577475
#> [100,] 0.73630691 0.26369309
#> [101,] 0.64714022 0.35285978
#> [102,] 0.70292667 0.29707333
#> [103,] 0.89340121 0.10659879
#> [104,] 0.91034619 0.08965381
#> [105,] 0.91065834 0.08934166
#> [106,] 0.89340121 0.10659879
#> [107,] 0.74734903 0.25265097
#> [108,] 0.79258048 0.20741952
#> [109,] 0.91065834 0.08934166
#> [110,] 0.88164477 0.11835523
#> [111,] 0.55568163 0.44431837
#> [112,] 0.69605852 0.30394148
#> [113,] 0.62462713 0.37537287
#> [114,] 0.83204666 0.16795334
#> [115,] 0.68570432 0.31429568
#> [116,] 0.83235069 0.16764931
#> [117,] 0.80405955 0.19594045
#> [118,] 0.91065834 0.08934166
#> [119,] 0.63538378 0.36461622
#> [120,] 0.74269688 0.25730312
#> [121,] 0.89798640 0.10201360
#> [122,] 1.00000000 0.00000000
#> [123,] 1.00000000 0.00000000
#> [124,] 1.00000000 0.00000000
#> [125,] 0.81492670 0.18507330
#> [126,] 0.91065834 0.08934166
#> [127,] 0.81509356 0.18490644
#> [128,] 0.92169235 0.07830765
#> [129,] 0.92169235 0.07830765
#> [130,] 0.92169235 0.07830765
#> [131,] 0.79199429 0.20800571
#> [132,] 0.82596071 0.17403929
#> [133,] 0.79199429 0.20800571
#> [134,] 0.81509356 0.18490644
#> [135,] 0.80941828 0.19058172
#> [136,] 0.90426836 0.09573164
#> [137,] 0.80405955 0.19594045
#> [138,] 0.92169235 0.07830765
#> [139,] 0.73080660 0.26919340
#>
#> $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" "M" "M" "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" "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
#> 0.0000000 3.9996973 9.6872276 13.1057449 0.0000000 0.7563481 0.0000000
#> V16 V17 V18 V19 V2 V20 V21
#> 1.9629123 0.0000000 0.0000000 0.8965904 1.7733259 0.0000000 4.6783699
#> V22 V23 V24 V25 V26 V27 V28
#> 0.0000000 0.9569667 0.0000000 0.8630666 0.0000000 0.0000000 0.0000000
#> V29 V3 V30 V31 V32 V33 V34
#> 0.0000000 0.0000000 0.0000000 5.1680839 0.0000000 0.0000000 2.1999245
#> V35 V36 V37 V38 V39 V4 V40
#> 3.0899576 6.2599293 2.6468340 0.0000000 2.0682169 3.8518974 1.0640305
#> V41 V42 V43 V44 V45 V46 V47
#> 0.0000000 2.4944941 0.0000000 0.0000000 4.5267188 0.0000000 4.4564633
#> V48 V49 V5 V50 V51 V52 V53
#> 1.2230247 0.0000000 2.5583943 1.1305093 6.9900141 0.0000000 0.0000000
#> V54 V55 V56 V57 V58 V59 V6
#> 3.0303466 0.0000000 0.0000000 0.0000000 1.0677777 1.2421615 1.7506111
#> V60 V7 V8 V9
#> 1.9775075 0.0000000 0.0000000 2.5228532
#>
#> $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"
#>
#> $call
#> adabag::boosting(formula = formula, data = data, mfinal = 10L,
#> control = list(minsplit = 20L, minbucket = 7, cp = 0.01,
#> maxcompete = 4L, maxsurrogate = 5L, usesurrogate = 2L,
#> surrogatestyle = 0L, maxdepth = 30L, xval = 0L))
#>
#> attr(,"vardep.summary")
#> M R
#> 74 65
#> attr(,"class")
#> [1] "boosting"
print(learner$importance())
#> V12 V11 V51 V36 V31 V21 V45
#> 13.1057449 9.6872276 6.9900141 6.2599293 5.1680839 4.6783699 4.5267188
#> V47 V10 V4 V35 V54 V37 V5
#> 4.4564633 3.9996973 3.8518974 3.0899576 3.0303466 2.6468340 2.5583943
#> V9 V42 V34 V39 V60 V16 V2
#> 2.5228532 2.4944941 2.1999245 2.0682169 1.9775075 1.9629123 1.7733259
#> V6 V59 V48 V50 V58 V40 V23
#> 1.7506111 1.2421615 1.2230247 1.1305093 1.0677777 1.0640305 0.9569667
#> V19 V25 V14 V1 V13 V15 V17
#> 0.8965904 0.8630666 0.7563481 0.0000000 0.0000000 0.0000000 0.0000000
#> V18 V20 V22 V24 V26 V27 V28
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V29 V3 V30 V32 V33 V38 V41
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V43 V44 V46 V49 V52 V53 V55
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V56 V57 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
#> 0.2028986