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 63 R (0.45323741 0.54676259)
#> 2) V12>=0.1941 75 22 M (0.70666667 0.29333333)
#> 4) V34< 0.67925 65 12 M (0.81538462 0.18461538)
#> 8) V43>=0.1883 49 2 M (0.95918367 0.04081633) *
#> 9) V43< 0.1883 16 6 R (0.37500000 0.62500000) *
#> 5) V34>=0.67925 10 0 R (0.00000000 1.00000000) *
#> 3) V12< 0.1941 64 10 R (0.15625000 0.84375000)
#> 6) V46>=0.1639 9 2 M (0.77777778 0.22222222) *
#> 7) V46< 0.1639 55 3 R (0.05454545 0.94545455) *
#>
#> $trees[[2]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 64 M (0.53956835 0.46043165)
#> 2) V49>=0.03625 84 17 M (0.79761905 0.20238095)
#> 4) V37< 0.4634 61 2 M (0.96721311 0.03278689) *
#> 5) V37>=0.4634 23 8 R (0.34782609 0.65217391)
#> 10) V49>=0.0755 9 1 M (0.88888889 0.11111111) *
#> 11) V49< 0.0755 14 0 R (0.00000000 1.00000000) *
#> 3) V49< 0.03625 55 8 R (0.14545455 0.85454545) *
#>
#> $trees[[3]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 69 M (0.50359712 0.49640288)
#> 2) V11>=0.21675 68 13 M (0.80882353 0.19117647)
#> 4) V15< 0.645 56 5 M (0.91071429 0.08928571) *
#> 5) V15>=0.645 12 4 R (0.33333333 0.66666667) *
#> 3) V11< 0.21675 71 15 R (0.21126761 0.78873239)
#> 6) V5>=0.099 13 3 M (0.76923077 0.23076923) *
#> 7) V5< 0.099 58 5 R (0.08620690 0.91379310) *
#>
#> $trees[[4]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 68 M (0.51079137 0.48920863)
#> 2) V21>=0.6469 82 25 M (0.69512195 0.30487805)
#> 4) V38>=0.2283 55 7 M (0.87272727 0.12727273)
#> 8) V31< 0.5757 48 2 M (0.95833333 0.04166667) *
#> 9) V31>=0.5757 7 2 R (0.28571429 0.71428571) *
#> 5) V38< 0.2283 27 9 R (0.33333333 0.66666667)
#> 10) V56< 0.00485 7 0 M (1.00000000 0.00000000) *
#> 11) V56>=0.00485 20 2 R (0.10000000 0.90000000) *
#> 3) V21< 0.6469 57 14 R (0.24561404 0.75438596)
#> 6) V36< 0.21165 12 3 M (0.75000000 0.25000000) *
#> 7) V36>=0.21165 45 5 R (0.11111111 0.88888889)
#> 14) V52>=0.0189 9 4 M (0.55555556 0.44444444) *
#> 15) V52< 0.0189 36 0 R (0.00000000 1.00000000) *
#>
#> $trees[[5]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 65 R (0.46762590 0.53237410)
#> 2) V11>=0.15105 107 44 M (0.58878505 0.41121495)
#> 4) V51>=0.0109 73 19 M (0.73972603 0.26027397)
#> 8) V21>=0.5274 52 5 M (0.90384615 0.09615385)
#> 16) V31>=0.21455 45 0 M (1.00000000 0.00000000) *
#> 17) V31< 0.21455 7 2 R (0.28571429 0.71428571) *
#> 9) V21< 0.5274 21 7 R (0.33333333 0.66666667)
#> 18) V27>=0.83755 7 0 M (1.00000000 0.00000000) *
#> 19) V27< 0.83755 14 0 R (0.00000000 1.00000000) *
#> 5) V51< 0.0109 34 9 R (0.26470588 0.73529412)
#> 10) V7< 0.087 8 0 M (1.00000000 0.00000000) *
#> 11) V7>=0.087 26 1 R (0.03846154 0.96153846) *
#> 3) V11< 0.15105 32 2 R (0.06250000 0.93750000) *
#>
#> $trees[[6]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 53 M (0.61870504 0.38129496)
#> 2) V32>=0.21445 108 28 M (0.74074074 0.25925926)
#> 4) V36< 0.47405 73 6 M (0.91780822 0.08219178) *
#> 5) V36>=0.47405 35 13 R (0.37142857 0.62857143)
#> 10) V45>=0.26365 11 0 M (1.00000000 0.00000000) *
#> 11) V45< 0.26365 24 2 R (0.08333333 0.91666667) *
#> 3) V32< 0.21445 31 6 R (0.19354839 0.80645161)
#> 6) V43>=0.2171 8 2 M (0.75000000 0.25000000) *
#> 7) V43< 0.2171 23 0 R (0.00000000 1.00000000) *
#>
#> $trees[[7]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 68 M (0.51079137 0.48920863)
#> 2) V12>=0.1786 97 31 M (0.68041237 0.31958763)
#> 4) V24>=0.57065 71 13 M (0.81690141 0.18309859)
#> 8) V5>=0.04175 54 4 M (0.92592593 0.07407407) *
#> 9) V5< 0.04175 17 8 R (0.47058824 0.52941176) *
#> 5) V24< 0.57065 26 8 R (0.30769231 0.69230769)
#> 10) V34>=0.3931 10 3 M (0.70000000 0.30000000) *
#> 11) V34< 0.3931 16 1 R (0.06250000 0.93750000) *
#> 3) V12< 0.1786 42 5 R (0.11904762 0.88095238)
#> 6) V53< 0.00515 9 4 M (0.55555556 0.44444444) *
#> 7) V53>=0.00515 33 0 R (0.00000000 1.00000000) *
#>
#> $trees[[8]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 63 R (0.45323741 0.54676259)
#> 2) V45>=0.19565 58 17 M (0.70689655 0.29310345)
#> 4) V21>=0.54295 37 1 M (0.97297297 0.02702703) *
#> 5) V21< 0.54295 21 5 R (0.23809524 0.76190476)
#> 10) V12>=0.1942 7 2 M (0.71428571 0.28571429) *
#> 11) V12< 0.1942 14 0 R (0.00000000 1.00000000) *
#> 3) V45< 0.19565 81 22 R (0.27160494 0.72839506)
#> 6) V28>=0.9405 8 0 M (1.00000000 0.00000000) *
#> 7) V28< 0.9405 73 14 R (0.19178082 0.80821918)
#> 14) V52>=0.01455 25 11 R (0.44000000 0.56000000)
#> 28) V11>=0.21325 9 0 M (1.00000000 0.00000000) *
#> 29) V11< 0.21325 16 2 R (0.12500000 0.87500000) *
#> 15) V52< 0.01455 48 3 R (0.06250000 0.93750000) *
#>
#> $trees[[9]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 60 R (0.43165468 0.56834532)
#> 2) V54>=0.0225 18 0 M (1.00000000 0.00000000) *
#> 3) V54< 0.0225 121 42 R (0.34710744 0.65289256)
#> 6) V35< 0.36175 66 30 M (0.54545455 0.45454545)
#> 12) V56< 0.00555 26 2 M (0.92307692 0.07692308) *
#> 13) V56>=0.00555 40 12 R (0.30000000 0.70000000)
#> 26) V60>=0.00535 18 7 M (0.61111111 0.38888889) *
#> 27) V60< 0.00535 22 1 R (0.04545455 0.95454545) *
#> 7) V35>=0.36175 55 6 R (0.10909091 0.89090909)
#> 14) V27>=0.9351 9 4 M (0.55555556 0.44444444) *
#> 15) V27< 0.9351 46 1 R (0.02173913 0.97826087) *
#>
#> $trees[[10]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 65 R (0.46762590 0.53237410)
#> 2) V27< 0.50195 39 8 M (0.79487179 0.20512821)
#> 4) V14>=0.14685 29 1 M (0.96551724 0.03448276) *
#> 5) V14< 0.14685 10 3 R (0.30000000 0.70000000) *
#> 3) V27>=0.50195 100 34 R (0.34000000 0.66000000)
#> 6) V26>=0.9596 14 0 M (1.00000000 0.00000000) *
#> 7) V26< 0.9596 86 20 R (0.23255814 0.76744186)
#> 14) V43>=0.3442 15 5 M (0.66666667 0.33333333) *
#> 15) V43< 0.3442 71 10 R (0.14084507 0.85915493)
#> 30) V50< 0.0086 8 2 M (0.75000000 0.25000000) *
#> 31) V50>=0.0086 63 4 R (0.06349206 0.93650794)
#> 62) V54>=0.0169 7 3 M (0.57142857 0.42857143) *
#> 63) V54< 0.0169 56 0 R (0.00000000 1.00000000) *
#>
#>
#> $weights
#> [1] 0.6450753 0.6224661 0.5576063 0.7047701 0.7808891 0.7539233 0.6294120
#> [8] 0.7603183 0.6346207 0.6934969
#>
#> $votes
#> [,1] [,2]
#> [1,] 1.5142416 5.2683365
#> [2,] 2.0698863 4.7126918
#> [3,] 2.1593168 4.6232612
#> [4,] 2.1593168 4.6232612
#> [5,] 3.3999656 3.3826125
#> [6,] 1.9731162 4.8094619
#> [7,] 2.6521068 4.1304713
#> [8,] 2.7157803 4.0667978
#> [9,] 2.1521904 4.6303877
#> [10,] 2.1521904 4.6303877
#> [11,] 2.0328878 4.7496903
#> [12,] 1.8961785 4.8863996
#> [13,] 2.6521068 4.1304713
#> [14,] 2.5904941 4.1920840
#> [15,] 1.9575296 4.8250485
#> [16,] 2.5194633 4.2631148
#> [17,] 1.9473366 4.8352415
#> [18,] 1.8805152 4.9020629
#> [19,] 2.6508767 4.1317014
#> [20,] 1.2744873 5.5080908
#> [21,] 1.3989986 5.3835795
#> [22,] 1.3833353 5.3992427
#> [23,] 2.0432656 4.7393125
#> [24,] 1.8857240 4.8968541
#> [25,] 0.6450753 6.1375028
#> [26,] 2.2395825 4.5429955
#> [27,] 2.0328878 4.7496903
#> [28,] 1.9610382 4.8215399
#> [29,] 1.4474202 5.3351579
#> [30,] 2.6573155 4.1252626
#> [31,] 0.0000000 6.7825781
#> [32,] 1.3229089 5.4596692
#> [33,] 1.4474202 5.3351579
#> [34,] 1.3159629 5.4666152
#> [35,] 0.7539233 6.0286548
#> [36,] 1.8805152 4.9020629
#> [37,] 1.9473366 4.8352415
#> [38,] 2.0433423 4.7392358
#> [39,] 3.2303606 3.5522175
#> [40,] 2.0110102 4.7715679
#> [41,] 0.6294120 6.1531661
#> [42,] 1.3281176 5.4544605
#> [43,] 0.0000000 6.7825781
#> [44,] 1.3949390 5.3876391
#> [45,] 0.6294120 6.1531661
#> [46,] 0.6294120 6.1531661
#> [47,] 0.6294120 6.1531661
#> [48,] 1.3229089 5.4596692
#> [49,] 0.6294120 6.1531661
#> [50,] 1.9575296 4.8250485
#> [51,] 2.0328878 4.7496903
#> [52,] 2.0820410 4.7005371
#> [53,] 1.4474202 5.3351579
#> [54,] 0.7539233 6.0286548
#> [55,] 1.3763894 5.4061887
#> [56,] 0.7539233 6.0286548
#> [57,] 1.9731162 4.8094619
#> [58,] 2.7866905 3.9958876
#> [59,] 1.9339957 4.8485823
#> [60,] 2.6508767 4.1317014
#> [61,] 2.0484304 4.7341477
#> [62,] 1.2511032 5.5314749
#> [63,] 2.0276790 4.7548991
#> [64,] 0.7603183 6.0222598
#> [65,] 2.0162998 4.7662783
#> [66,] 0.7539233 6.0286548
#> [67,] 2.0933142 4.6892639
#> [68,] 3.9083748 2.8742033
#> [69,] 5.3566138 1.4259643
#> [70,] 5.5028821 1.2796960
#> [71,] 4.7547185 2.0278596
#> [72,] 4.8804160 1.9021621
#> [73,] 6.1601120 0.6224661
#> [74,] 5.3670683 1.4155098
#> [75,] 5.3997938 1.3827843
#> [76,] 4.6119588 2.1706193
#> [77,] 5.2413708 1.5412073
#> [78,] 6.0222598 0.7603183
#> [79,] 6.7825781 0.0000000
#> [80,] 4.7446022 2.0379759
#> [81,] 5.3835795 1.3989986
#> [82,] 6.0778079 0.7047701
#> [83,] 5.4544605 1.3281176
#> [84,] 5.5903510 1.1922271
#> [85,] 5.3174897 1.4650884
#> [86,] 6.0890812 0.6934969
#> [87,] 5.5150368 1.2675413
#> [88,] 4.8863996 1.8961785
#> [89,] 4.1304713 2.6521068
#> [90,] 5.4646535 1.3179246
#> [91,] 4.8146707 1.9679074
#> [92,] 6.1375028 0.6450753
#> [93,] 4.9574304 1.8251477
#> [94,] 5.3722770 1.4103011
#> [95,] 5.5254913 1.2570868
#> [96,] 5.5185453 1.2640328
#> [97,] 5.2413708 1.5412073
#> [98,] 5.5955597 1.1870184
#> [99,] 6.2249717 0.5576063
#> [100,] 4.8137752 1.9688029
#> [101,] 6.1531661 0.6294120
#> [102,] 4.7393125 2.0432656
#> [103,] 3.5036153 3.2789628
#> [104,] 4.6836877 2.0988904
#> [105,] 5.5185453 1.2640328
#> [106,] 4.8216166 1.9609615
#> [107,] 4.7990074 1.9835707
#> [108,] 6.0778079 0.7047701
#> [109,] 5.4431872 1.3393909
#> [110,] 6.1479573 0.6346207
#> [111,] 6.0778079 0.7047701
#> [112,] 6.1479573 0.6346207
#> [113,] 4.0377937 2.7447844
#> [114,] 4.0491437 2.7334344
#> [115,] 4.8216166 1.9609615
#> [116,] 5.6025057 1.1800724
#> [117,] 6.1601120 0.6224661
#> [118,] 4.6950236 2.0875545
#> [119,] 5.3792230 1.4033551
#> [120,] 4.7703818 2.0121963
#> [121,] 4.9504845 1.8320936
#> [122,] 6.0778079 0.7047701
#> [123,] 6.0890812 0.6934969
#> [124,] 6.1479573 0.6346207
#> [125,] 6.0778079 0.7047701
#> [126,] 6.7825781 0.0000000
#> [127,] 5.3722770 1.4103011
#> [128,] 5.3670683 1.4155098
#> [129,] 6.1531661 0.6294120
#> [130,] 4.8093852 1.9731929
#> [131,] 5.4440060 1.3385721
#> [132,] 5.5028821 1.2796960
#> [133,] 5.3722770 1.4103011
#> [134,] 6.7825781 0.0000000
#> [135,] 5.4431872 1.3393909
#> [136,] 6.7825781 0.0000000
#> [137,] 6.0890812 0.6934969
#> [138,] 5.3566138 1.4259643
#> [139,] 5.5307000 1.2518781
#>
#> $prob
#> [,1] [,2]
#> [1,] 0.22325458 0.77674542
#> [2,] 0.30517692 0.69482308
#> [3,] 0.31836225 0.68163775
#> [4,] 0.31836225 0.68163775
#> [5,] 0.50127924 0.49872076
#> [6,] 0.29090946 0.70909054
#> [7,] 0.39101750 0.60898250
#> [8,] 0.40040531 0.59959469
#> [9,] 0.31731155 0.68268845
#> [10,] 0.31731155 0.68268845
#> [11,] 0.29972198 0.70027802
#> [12,] 0.27956604 0.72043396
#> [13,] 0.39101750 0.60898250
#> [14,] 0.38193355 0.61806645
#> [15,] 0.28861144 0.71138856
#> [16,] 0.37146101 0.62853899
#> [17,] 0.28710862 0.71289138
#> [18,] 0.27725670 0.72274330
#> [19,] 0.39083614 0.60916386
#> [20,] 0.18790602 0.81209398
#> [21,] 0.20626354 0.79373646
#> [22,] 0.20395421 0.79604579
#> [23,] 0.30125205 0.69874795
#> [24,] 0.27802466 0.72197534
#> [25,] 0.09510768 0.90489232
#> [26,] 0.33019635 0.66980365
#> [27,] 0.29972198 0.70027802
#> [28,] 0.28912873 0.71087127
#> [29,] 0.21340266 0.78659734
#> [30,] 0.39178546 0.60821454
#> [31,] 0.00000000 1.00000000
#> [32,] 0.19504514 0.80495486
#> [33,] 0.21340266 0.78659734
#> [34,] 0.19402105 0.80597895
#> [35,] 0.11115587 0.88884413
#> [36,] 0.27725670 0.72274330
#> [37,] 0.28710862 0.71289138
#> [38,] 0.30126336 0.69873664
#> [39,] 0.47627327 0.52372673
#> [40,] 0.29649642 0.70350358
#> [41,] 0.09279834 0.90720166
#> [42,] 0.19581310 0.80418690
#> [43,] 0.00000000 1.00000000
#> [44,] 0.20566501 0.79433499
#> [45,] 0.09279834 0.90720166
#> [46,] 0.09279834 0.90720166
#> [47,] 0.09279834 0.90720166
#> [48,] 0.19504514 0.80495486
#> [49,] 0.09279834 0.90720166
#> [50,] 0.28861144 0.71138856
#> [51,] 0.29972198 0.70027802
#> [52,] 0.30696896 0.69303104
#> [53,] 0.21340266 0.78659734
#> [54,] 0.11115587 0.88884413
#> [55,] 0.20293012 0.79706988
#> [56,] 0.11115587 0.88884413
#> [57,] 0.29090946 0.70909054
#> [58,] 0.41086007 0.58913993
#> [59,] 0.28514169 0.71485831
#> [60,] 0.39083614 0.60916386
#> [61,] 0.30201353 0.69798647
#> [62,] 0.18445836 0.81554164
#> [63,] 0.29895403 0.70104597
#> [64,] 0.11209871 0.88790129
#> [65,] 0.29727632 0.70272368
#> [66,] 0.11115587 0.88884413
#> [67,] 0.30863105 0.69136895
#> [68,] 0.57623735 0.42376265
#> [69,] 0.78976072 0.21023928
#> [70,] 0.81132602 0.18867398
#> [71,] 0.70101936 0.29898064
#> [72,] 0.71955176 0.28044824
#> [73,] 0.90822574 0.09177426
#> [74,] 0.79130210 0.20869790
#> [75,] 0.79612703 0.20387297
#> [76,] 0.67997135 0.32002865
#> [77,] 0.77276969 0.22723031
#> [78,] 0.88790129 0.11209871
#> [79,] 1.00000000 0.00000000
#> [80,] 0.69952784 0.30047216
#> [81,] 0.79373646 0.20626354
#> [82,] 0.89609111 0.10390889
#> [83,] 0.80418690 0.19581310
#> [84,] 0.82422214 0.17577786
#> [85,] 0.78399240 0.21600760
#> [86,] 0.89775320 0.10224680
#> [87,] 0.81311807 0.18688193
#> [88,] 0.72043396 0.27956604
#> [89,] 0.60898250 0.39101750
#> [90,] 0.80568973 0.19431027
#> [91,] 0.70985849 0.29014151
#> [92,] 0.90489232 0.09510768
#> [93,] 0.73090650 0.26909350
#> [94,] 0.79207006 0.20792994
#> [95,] 0.81465944 0.18534056
#> [96,] 0.81363536 0.18636464
#> [97,] 0.77276969 0.22723031
#> [98,] 0.82499009 0.17500991
#> [99,] 0.91778844 0.08221156
#> [100,] 0.70972647 0.29027353
#> [101,] 0.90720166 0.09279834
#> [102,] 0.69874795 0.30125205
#> [103,] 0.51656100 0.48343900
#> [104,] 0.69054682 0.30945318
#> [105,] 0.81363536 0.18636464
#> [106,] 0.71088258 0.28911742
#> [107,] 0.70754916 0.29245084
#> [108,] 0.89609111 0.10390889
#> [109,] 0.80252481 0.19747519
#> [110,] 0.90643370 0.09356630
#> [111,] 0.89609111 0.10390889
#> [112,] 0.90643370 0.09356630
#> [113,] 0.59531842 0.40468158
#> [114,] 0.59699183 0.40300817
#> [115,] 0.71088258 0.28911742
#> [116,] 0.82601418 0.17398582
#> [117,] 0.90822574 0.09177426
#> [118,] 0.69221815 0.30778185
#> [119,] 0.79309414 0.20690586
#> [120,] 0.70332869 0.29667131
#> [121,] 0.72988242 0.27011758
#> [122,] 0.89609111 0.10390889
#> [123,] 0.89775320 0.10224680
#> [124,] 0.90643370 0.09356630
#> [125,] 0.89609111 0.10390889
#> [126,] 1.00000000 0.00000000
#> [127,] 0.79207006 0.20792994
#> [128,] 0.79130210 0.20869790
#> [129,] 0.90720166 0.09279834
#> [130,] 0.70907923 0.29092077
#> [131,] 0.80264553 0.19735447
#> [132,] 0.81132602 0.18867398
#> [133,] 0.79207006 0.20792994
#> [134,] 1.00000000 0.00000000
#> [135,] 0.80252481 0.19747519
#> [136,] 1.00000000 0.00000000
#> [137,] 0.89775320 0.10224680
#> [138,] 0.78976072 0.21023928
#> [139,] 0.81542740 0.18457260
#>
#> $class
#> [1] "R" "R" "R" "R" "M" "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" "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 0.0000000 9.2245192 8.5004977 0.0000000 1.3508641 1.0864960 0.0000000
#> V17 V18 V19 V2 V20 V21 V22 V23
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 8.3342082 0.0000000 0.0000000
#> V24 V25 V26 V27 V28 V29 V3 V30
#> 1.8369716 0.0000000 2.9085539 5.3415736 2.1178573 0.0000000 0.0000000 0.0000000
#> V31 V32 V33 V34 V35 V36 V37 V38
#> 2.5794992 3.2158296 0.0000000 3.1293955 2.1441135 4.7610657 2.3590298 2.1962783
#> V39 V4 V40 V41 V42 V43 V44 V45
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 4.4635978 0.0000000 5.7060351
#> V46 V47 V48 V49 V5 V50 V51 V52
#> 1.5434992 0.0000000 0.0000000 6.7979801 2.6318418 1.3722176 2.4173890 1.9797199
#> V53 V54 V55 V56 V57 V58 V59 V6
#> 0.8124793 3.3403624 0.0000000 4.0468609 0.0000000 0.0000000 0.0000000 0.0000000
#> V60 V7 V8 V9
#> 1.1889683 2.6122951 0.0000000 0.0000000
#>
#> $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())
#> V11 V12 V21 V49 V45 V27 V36 V43
#> 9.2245192 8.5004977 8.3342082 6.7979801 5.7060351 5.3415736 4.7610657 4.4635978
#> V56 V54 V32 V34 V26 V5 V7 V31
#> 4.0468609 3.3403624 3.2158296 3.1293955 2.9085539 2.6318418 2.6122951 2.5794992
#> V51 V37 V38 V35 V28 V52 V24 V46
#> 2.4173890 2.3590298 2.1962783 2.1441135 2.1178573 1.9797199 1.8369716 1.5434992
#> V50 V14 V60 V15 V53 V1 V10 V13
#> 1.3722176 1.3508641 1.1889683 1.0864960 0.8124793 0.0000000 0.0000000 0.0000000
#> V16 V17 V18 V19 V2 V20 V22 V23
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V25 V29 V3 V30 V33 V39 V4 V40
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V41 V42 V44 V47 V48 V55 V57 V58
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V59 V6 V8 V9
#> 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
#> 0.2173913