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 R (0.43884892 0.56115108)
#> 2) V11>=0.1998 75 22 M (0.70666667 0.29333333)
#> 4) V52>=0.0069 64 12 M (0.81250000 0.18750000)
#> 8) V29>=0.447 51 3 M (0.94117647 0.05882353) *
#> 9) V29< 0.447 13 4 R (0.30769231 0.69230769) *
#> 5) V52< 0.0069 11 1 R (0.09090909 0.90909091) *
#> 3) V11< 0.1998 64 8 R (0.12500000 0.87500000)
#> 6) V1>=0.0392 10 3 M (0.70000000 0.30000000) *
#> 7) V1< 0.0392 54 1 R (0.01851852 0.98148148) *
#>
#> $trees[[2]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 67 R (0.48201439 0.51798561)
#> 2) V49>=0.03685 76 22 M (0.71052632 0.28947368)
#> 4) V51>=0.01285 54 6 M (0.88888889 0.11111111) *
#> 5) V51< 0.01285 22 6 R (0.27272727 0.72727273)
#> 10) V28>=0.75935 7 2 M (0.71428571 0.28571429) *
#> 11) V28< 0.75935 15 1 R (0.06666667 0.93333333) *
#> 3) V49< 0.03685 63 13 R (0.20634921 0.79365079)
#> 6) V28< 0.43995 9 1 M (0.88888889 0.11111111) *
#> 7) V28>=0.43995 54 5 R (0.09259259 0.90740741) *
#>
#> $trees[[3]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 67 R (0.48201439 0.51798561)
#> 2) V43>=0.35065 29 3 M (0.89655172 0.10344828) *
#> 3) V43< 0.35065 110 41 R (0.37272727 0.62727273)
#> 6) V36< 0.48825 66 27 M (0.59090909 0.40909091)
#> 12) V1>=0.0157 49 12 M (0.75510204 0.24489796)
#> 24) V23>=0.7542 28 2 M (0.92857143 0.07142857) *
#> 25) V23< 0.7542 21 10 M (0.52380952 0.47619048)
#> 50) V28< 0.53015 9 1 M (0.88888889 0.11111111) *
#> 51) V28>=0.53015 12 3 R (0.25000000 0.75000000) *
#> 13) V1< 0.0157 17 2 R (0.11764706 0.88235294) *
#> 7) V36>=0.48825 44 2 R (0.04545455 0.95454545) *
#>
#> $trees[[4]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 62 M (0.55395683 0.44604317)
#> 2) V11>=0.1534 95 25 M (0.73684211 0.26315789)
#> 4) V40< 0.44795 75 10 M (0.86666667 0.13333333)
#> 8) V7< 0.20865 68 4 M (0.94117647 0.05882353) *
#> 9) V7>=0.20865 7 1 R (0.14285714 0.85714286) *
#> 5) V40>=0.44795 20 5 R (0.25000000 0.75000000)
#> 10) V21>=0.80365 8 3 M (0.62500000 0.37500000) *
#> 11) V21< 0.80365 12 0 R (0.00000000 1.00000000) *
#> 3) V11< 0.1534 44 7 R (0.15909091 0.84090909)
#> 6) V53< 0.00535 11 5 M (0.54545455 0.45454545) *
#> 7) V53>=0.00535 33 1 R (0.03030303 0.96969697) *
#>
#> $trees[[5]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 62 R (0.44604317 0.55395683)
#> 2) V9>=0.15095 57 16 M (0.71929825 0.28070175)
#> 4) V17< 0.4374 29 1 M (0.96551724 0.03448276) *
#> 5) V17>=0.4374 28 13 R (0.46428571 0.53571429)
#> 10) V36< 0.2923 17 4 M (0.76470588 0.23529412) *
#> 11) V36>=0.2923 11 0 R (0.00000000 1.00000000) *
#> 3) V9< 0.15095 82 21 R (0.25609756 0.74390244)
#> 6) V4>=0.0611 13 1 M (0.92307692 0.07692308) *
#> 7) V4< 0.0611 69 9 R (0.13043478 0.86956522)
#> 14) V28>=0.96735 10 1 M (0.90000000 0.10000000) *
#> 15) V28< 0.96735 59 0 R (0.00000000 1.00000000) *
#>
#> $trees[[6]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 64 M (0.53956835 0.46043165)
#> 2) V51>=0.01365 78 20 M (0.74358974 0.25641026)
#> 4) V12>=0.14575 62 8 M (0.87096774 0.12903226)
#> 8) V7< 0.21185 53 2 M (0.96226415 0.03773585) *
#> 9) V7>=0.21185 9 3 R (0.33333333 0.66666667) *
#> 5) V12< 0.14575 16 4 R (0.25000000 0.75000000) *
#> 3) V51< 0.01365 61 17 R (0.27868852 0.72131148)
#> 6) V38>=0.42275 23 9 M (0.60869565 0.39130435)
#> 12) V31< 0.53425 13 0 M (1.00000000 0.00000000) *
#> 13) V31>=0.53425 10 1 R (0.10000000 0.90000000) *
#> 7) V38< 0.42275 38 3 R (0.07894737 0.92105263) *
#>
#> $trees[[7]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 60 R (0.4316547 0.5683453)
#> 2) V45>=0.26365 37 6 M (0.8378378 0.1621622)
#> 4) V40< 0.58555 30 0 M (1.0000000 0.0000000) *
#> 5) V40>=0.58555 7 1 R (0.1428571 0.8571429) *
#> 3) V45< 0.26365 102 29 R (0.2843137 0.7156863)
#> 6) V12>=0.1922 65 29 R (0.4461538 0.5538462)
#> 12) V31< 0.4457 21 4 M (0.8095238 0.1904762)
#> 24) V31>=0.29095 14 0 M (1.0000000 0.0000000) *
#> 25) V31< 0.29095 7 3 R (0.4285714 0.5714286) *
#> 13) V31>=0.4457 44 12 R (0.2727273 0.7272727)
#> 26) V57< 0.00585 10 2 M (0.8000000 0.2000000) *
#> 27) V57>=0.00585 34 4 R (0.1176471 0.8823529)
#> 54) V18< 0.277 7 3 M (0.5714286 0.4285714) *
#> 55) V18>=0.277 27 0 R (0.0000000 1.0000000) *
#> 7) V12< 0.1922 37 0 R (0.0000000 1.0000000) *
#>
#> $trees[[8]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 65 R (0.46762590 0.53237410)
#> 2) V45>=0.38545 29 0 M (1.00000000 0.00000000) *
#> 3) V45< 0.38545 110 36 R (0.32727273 0.67272727)
#> 6) V42>=0.26545 48 20 M (0.58333333 0.41666667)
#> 12) V36< 0.5274 32 5 M (0.84375000 0.15625000) *
#> 13) V36>=0.5274 16 1 R (0.06250000 0.93750000) *
#> 7) V42< 0.26545 62 8 R (0.12903226 0.87096774)
#> 14) V37< 0.12475 9 4 M (0.55555556 0.44444444) *
#> 15) V37>=0.12475 53 3 R (0.05660377 0.94339623) *
#>
#> $trees[[9]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 69 R (0.49640288 0.50359712)
#> 2) V49>=0.05255 62 16 M (0.74193548 0.25806452)
#> 4) V30>=0.63865 31 0 M (1.00000000 0.00000000) *
#> 5) V30< 0.63865 31 15 R (0.48387097 0.51612903)
#> 10) V36< 0.2913 11 0 M (1.00000000 0.00000000) *
#> 11) V36>=0.2913 20 4 R (0.20000000 0.80000000) *
#> 3) V49< 0.05255 77 23 R (0.29870130 0.70129870)
#> 6) V40< 0.10525 12 1 M (0.91666667 0.08333333) *
#> 7) V40>=0.10525 65 12 R (0.18461538 0.81538462)
#> 14) V20>=0.88045 11 4 M (0.63636364 0.36363636) *
#> 15) V20< 0.88045 54 5 R (0.09259259 0.90740741)
#> 30) V57< 0.0029 7 2 M (0.71428571 0.28571429) *
#> 31) V57>=0.0029 47 0 R (0.00000000 1.00000000) *
#>
#> $trees[[10]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 64 M (0.5395683 0.4604317)
#> 2) V56>=0.01015 50 6 M (0.8800000 0.1200000)
#> 4) V38>=0.24595 41 0 M (1.0000000 0.0000000) *
#> 5) V38< 0.24595 9 3 R (0.3333333 0.6666667) *
#> 3) V56< 0.01015 89 31 R (0.3483146 0.6516854)
#> 6) V51>=0.01545 37 14 M (0.6216216 0.3783784)
#> 12) V41< 0.26775 19 0 M (1.0000000 0.0000000) *
#> 13) V41>=0.26775 18 4 R (0.2222222 0.7777778) *
#> 7) V51< 0.01545 52 8 R (0.1538462 0.8461538) *
#>
#>
#> $weights
#> [1] 0.7586613 0.8405564 0.7094110 0.5948734 0.9071553 0.7267262 0.7902898
#> [8] 0.6697552 0.5080125 0.5539871
#>
#> $votes
#> [,1] [,2]
#> [1,] 1.4997007 5.5597275
#> [2,] 1.3126484 5.7467798
#> [3,] 2.8319112 4.2275170
#> [4,] 1.3945435 5.6648847
#> [5,] 1.1028859 5.9565424
#> [6,] 0.5080125 6.5514157
#> [7,] 1.0619996 5.9974286
#> [8,] 1.3945435 5.6648847
#> [9,] 1.5170160 5.5424122
#> [10,] 0.6697552 6.3896730
#> [11,] 1.7477117 5.3117165
#> [12,] 2.2756773 4.7837509
#> [13,] 2.4744380 4.5849902
#> [14,] 0.5948734 6.4645549
#> [15,] 2.9254794 4.1339488
#> [16,] 2.1438244 4.9156038
#> [17,] 2.7737097 4.2857185
#> [18,] 1.9434422 5.1159860
#> [19,] 2.4993673 4.5600609
#> [20,] 1.3851631 5.6742651
#> [21,] 2.0100412 5.0493870
#> [22,] 1.8931756 5.1662526
#> [23,] 0.5948734 6.4645549
#> [24,] 0.5080125 6.5514157
#> [25,] 2.8799311 4.1794971
#> [26,] 1.4354297 5.6239985
#> [27,] 0.0000000 7.0594282
#> [28,] 0.5948734 6.4645549
#> [29,] 0.0000000 7.0594282
#> [30,] 1.3042843 5.7551439
#> [31,] 2.5640283 4.4953999
#> [32,] 0.0000000 7.0594282
#> [33,] 0.5948734 6.4645549
#> [34,] 1.4853876 5.5740407
#> [35,] 1.3042843 5.7551439
#> [36,] 1.1028859 5.9565424
#> [37,] 0.7094110 6.3500173
#> [38,] 1.3791662 5.6802621
#> [39,] 0.5948734 6.4645549
#> [40,] 2.2257195 4.8337087
#> [41,] 1.1488605 5.9105678
#> [42,] 0.9071553 6.1522729
#> [43,] 0.5080125 6.5514157
#> [44,] 1.5020287 5.5573995
#> [45,] 0.9071553 6.1522729
#> [46,] 0.5948734 6.4645549
#> [47,] 1.8755867 5.1838415
#> [48,] 0.0000000 7.0594282
#> [49,] 2.0077132 5.0517150
#> [50,] 0.7902898 6.2691384
#> [51,] 1.7726411 5.2867872
#> [52,] 1.1777677 5.8816605
#> [53,] 0.5080125 6.5514157
#> [54,] 1.1028859 5.9565424
#> [55,] 1.2633981 5.7960301
#> [56,] 1.7714106 5.2880176
#> [57,] 1.3791662 5.6802621
#> [58,] 2.2197226 4.8397057
#> [59,] 1.7317548 5.3276734
#> [60,] 1.2983023 5.7611259
#> [61,] 2.7687025 4.2907257
#> [62,] 2.0100412 5.0493870
#> [63,] 2.0945741 4.9648541
#> [64,] 2.8811949 4.1782334
#> [65,] 0.8405564 6.2188718
#> [66,] 1.4853876 5.5740407
#> [67,] 1.4354297 5.6239985
#> [68,] 1.5992177 5.4602105
#> [69,] 1.5769105 5.4825177
#> [70,] 1.4600450 5.5993832
#> [71,] 0.5080125 6.5514157
#> [72,] 4.5600609 2.4993673
#> [73,] 4.3808623 2.6785659
#> [74,] 4.9381585 2.1212698
#> [75,] 4.5690121 2.4904161
#> [76,] 4.8407219 2.2187063
#> [77,] 4.1698692 2.8895590
#> [78,] 4.6033218 2.4561064
#> [79,] 4.9216007 2.1378275
#> [80,] 4.4075913 2.6518369
#> [81,] 5.1159860 1.9434422
#> [82,] 5.5982858 1.4611425
#> [83,] 4.9348494 2.1245788
#> [84,] 5.0024646 2.0569636
#> [85,] 4.8899723 2.1694559
#> [86,] 6.5054411 0.5539871
#> [87,] 5.7927544 1.2666738
#> [88,] 6.3896730 0.6697552
#> [89,] 5.7108593 1.3485689
#> [90,] 6.3896730 0.6697552
#> [91,] 6.3896730 0.6697552
#> [92,] 5.5993832 1.4600450
#> [93,] 5.0024646 2.0569636
#> [94,] 4.8330012 2.2264270
#> [95,] 5.0833434 1.9760848
#> [96,] 6.2691384 0.7902898
#> [97,] 4.3586625 2.7007658
#> [98,] 6.2188718 0.8405564
#> [99,] 5.2387673 1.8206609
#> [100,] 5.2298161 1.8296121
#> [101,] 7.0594282 0.0000000
#> [102,] 7.0594282 0.0000000
#> [103,] 6.3500173 0.7094110
#> [104,] 5.6648847 1.3945435
#> [105,] 4.4251802 2.6342480
#> [106,] 6.5054411 0.5539871
#> [107,] 6.5054411 0.5539871
#> [108,] 4.2867348 2.7726934
#> [109,] 4.9285306 2.1308977
#> [110,] 4.7837509 2.2756773
#> [111,] 5.7787149 1.2807134
#> [112,] 5.1838415 1.8755867
#> [113,] 5.9105678 1.1488605
#> [114,] 5.7787149 1.2807134
#> [115,] 5.1202780 1.9391503
#> [116,] 5.5982858 1.4611425
#> [117,] 5.2387673 1.8206609
#> [118,] 3.9258459 3.1335823
#> [119,] 4.3566172 2.7028110
#> [120,] 4.9951295 2.0642987
#> [121,] 4.2497169 2.8097114
#> [122,] 5.0693039 1.9901243
#> [123,] 7.0594282 0.0000000
#> [124,] 7.0594282 0.0000000
#> [125,] 7.0594282 0.0000000
#> [126,] 6.5054411 0.5539871
#> [127,] 5.8420048 1.2174235
#> [128,] 5.8420048 1.2174235
#> [129,] 5.8420048 1.2174235
#> [130,] 5.5094609 1.5499674
#> [131,] 5.0693039 1.9901243
#> [132,] 6.3500173 0.7094110
#> [133,] 5.7960301 1.2633981
#> [134,] 5.2707024 1.7887259
#> [135,] 5.8356859 1.2237423
#> [136,] 5.0200535 2.0393747
#> [137,] 7.0594282 0.0000000
#> [138,] 4.3995487 2.6598795
#> [139,] 5.5491166 1.5103116
#>
#> $prob
#> [,1] [,2]
#> [1,] 0.21243941 0.78756059
#> [2,] 0.18594260 0.81405740
#> [3,] 0.40115305 0.59884695
#> [4,] 0.19754341 0.80245659
#> [5,] 0.15622878 0.84377122
#> [6,] 0.07196227 0.92803773
#> [7,] 0.15043706 0.84956294
#> [8,] 0.19754341 0.80245659
#> [9,] 0.21489220 0.78510780
#> [10,] 0.09487386 0.90512614
#> [11,] 0.24757129 0.75242871
#> [12,] 0.32236001 0.67763999
#> [13,] 0.35051535 0.64948465
#> [14,] 0.08426651 0.91573349
#> [15,] 0.41440742 0.58559258
#> [16,] 0.30368245 0.69631755
#> [17,] 0.39290855 0.60709145
#> [18,] 0.27529740 0.72470260
#> [19,] 0.35404670 0.64595330
#> [20,] 0.19621463 0.80378537
#> [21,] 0.28473144 0.71526856
#> [22,] 0.26817691 0.73182309
#> [23,] 0.08426651 0.91573349
#> [24,] 0.07196227 0.92803773
#> [25,] 0.40795529 0.59204471
#> [26,] 0.20333513 0.79666487
#> [27,] 0.00000000 1.00000000
#> [28,] 0.08426651 0.91573349
#> [29,] 0.00000000 1.00000000
#> [30,] 0.18475778 0.81524222
#> [31,] 0.36320623 0.63679377
#> [32,] 0.00000000 1.00000000
#> [33,] 0.08426651 0.91573349
#> [34,] 0.21041188 0.78958812
#> [35,] 0.18475778 0.81524222
#> [36,] 0.15622878 0.84377122
#> [37,] 0.10049128 0.89950872
#> [38,] 0.19536514 0.80463486
#> [39,] 0.08426651 0.91573349
#> [40,] 0.31528326 0.68471674
#> [41,] 0.16274129 0.83725871
#> [42,] 0.12850267 0.87149733
#> [43,] 0.07196227 0.92803773
#> [44,] 0.21276917 0.78723083
#> [45,] 0.12850267 0.87149733
#> [46,] 0.08426651 0.91573349
#> [47,] 0.26568536 0.73431464
#> [48,] 0.00000000 1.00000000
#> [49,] 0.28440168 0.71559832
#> [50,] 0.11194813 0.88805187
#> [51,] 0.25110264 0.74889736
#> [52,] 0.16683613 0.83316387
#> [53,] 0.07196227 0.92803773
#> [54,] 0.15622878 0.84377122
#> [55,] 0.17896606 0.82103394
#> [56,] 0.25092834 0.74907166
#> [57,] 0.19536514 0.80463486
#> [58,] 0.31443376 0.68556624
#> [59,] 0.24531092 0.75468908
#> [60,] 0.18391040 0.81608960
#> [61,] 0.39219926 0.60780074
#> [62,] 0.28473144 0.71526856
#> [63,] 0.29670591 0.70329409
#> [64,] 0.40813431 0.59186569
#> [65,] 0.11906862 0.88093138
#> [66,] 0.21041188 0.78958812
#> [67,] 0.20333513 0.79666487
#> [68,] 0.22653644 0.77346356
#> [69,] 0.22337653 0.77662347
#> [70,] 0.20682199 0.79317801
#> [71,] 0.07196227 0.92803773
#> [72,] 0.64595330 0.35404670
#> [73,] 0.62056900 0.37943100
#> [74,] 0.69951252 0.30048748
#> [75,] 0.64722127 0.35277873
#> [76,] 0.68571020 0.31428980
#> [77,] 0.59068088 0.40931912
#> [78,] 0.65208139 0.34791861
#> [79,] 0.69716705 0.30283295
#> [80,] 0.62435528 0.37564472
#> [81,] 0.72470260 0.27529740
#> [82,] 0.79302255 0.20697745
#> [83,] 0.69904378 0.30095622
#> [84,] 0.70862179 0.29137821
#> [85,] 0.69268673 0.30731327
#> [86,] 0.92152521 0.07847479
#> [87,] 0.82056991 0.17943009
#> [88,] 0.90512614 0.09487386
#> [89,] 0.80896910 0.19103090
#> [90,] 0.90512614 0.09487386
#> [91,] 0.90512614 0.09487386
#> [92,] 0.79317801 0.20682199
#> [93,] 0.70862179 0.29137821
#> [94,] 0.68461653 0.31538347
#> [95,] 0.72007864 0.27992136
#> [96,] 0.88805187 0.11194813
#> [97,] 0.61742429 0.38257571
#> [98,] 0.88093138 0.11906862
#> [99,] 0.74209513 0.25790487
#> [100,] 0.74082715 0.25917285
#> [101,] 1.00000000 0.00000000
#> [102,] 1.00000000 0.00000000
#> [103,] 0.89950872 0.10049128
#> [104,] 0.80245659 0.19754341
#> [105,] 0.62684683 0.37315317
#> [106,] 0.92152521 0.07847479
#> [107,] 0.92152521 0.07847479
#> [108,] 0.60723541 0.39276459
#> [109,] 0.69814869 0.30185131
#> [110,] 0.67763999 0.32236001
#> [111,] 0.81858115 0.18141885
#> [112,] 0.73431464 0.26568536
#> [113,] 0.83725871 0.16274129
#> [114,] 0.81858115 0.18141885
#> [115,] 0.72531058 0.27468942
#> [116,] 0.79302255 0.20697745
#> [117,] 0.74209513 0.25790487
#> [118,] 0.55611386 0.44388614
#> [119,] 0.61713457 0.38286543
#> [120,] 0.70758273 0.29241727
#> [121,] 0.60199165 0.39800835
#> [122,] 0.71808987 0.28191013
#> [123,] 1.00000000 0.00000000
#> [124,] 1.00000000 0.00000000
#> [125,] 1.00000000 0.00000000
#> [126,] 0.92152521 0.07847479
#> [127,] 0.82754645 0.17245355
#> [128,] 0.82754645 0.17245355
#> [129,] 0.82754645 0.17245355
#> [130,] 0.78044010 0.21955990
#> [131,] 0.71808987 0.28191013
#> [132,] 0.89950872 0.10049128
#> [133,] 0.82103394 0.17896606
#> [134,] 0.74661887 0.25338113
#> [135,] 0.82665135 0.17334865
#> [136,] 0.71111333 0.28888667
#> [137,] 1.00000000 0.00000000
#> [138,] 0.62321601 0.37678399
#> [139,] 0.78605752 0.21394248
#>
#> $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" "R" "R" "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
#> 3.7616324 0.0000000 8.4409767 4.1382421 0.0000000 0.0000000 0.0000000 0.0000000
#> V17 V18 V19 V2 V20 V21 V22 V23
#> 1.8473137 0.8161955 0.0000000 0.0000000 0.7810533 0.6346391 0.0000000 0.7935594
#> V24 V25 V26 V27 V28 V29 V3 V30
#> 0.0000000 0.0000000 0.0000000 0.0000000 7.7190910 1.7945438 0.0000000 1.1935036
#> V31 V32 V33 V34 V35 V36 V37 V38
#> 4.4201936 0.0000000 0.0000000 0.0000000 0.0000000 8.9801282 0.7298960 2.6965090
#> V39 V4 V40 V41 V42 V43 V44 V45
#> 0.0000000 3.5474445 5.4765157 1.7625285 2.1278691 2.5418292 0.0000000 7.6986116
#> V46 V47 V48 V49 V5 V50 V51 V52
#> 0.0000000 0.0000000 0.0000000 6.1380165 0.0000000 0.0000000 7.3885210 2.1098058
#> V53 V54 V55 V56 V57 V58 V59 V6
#> 0.7410533 0.0000000 0.0000000 2.8526953 2.5163330 0.0000000 0.0000000 0.0000000
#> V60 V7 V8 V9
#> 0.0000000 2.6274248 0.0000000 3.7238742
#>
#> $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"
#>
#> $call
#> adabag::boosting(formula = formula, data = data, mfinal = 10L,
#> control = list(minsplit = 20L, minbucket = 7, cp = 0.01,
#> maxcompete = 4L, maxsurrogate = 5L, usesurrogate = 2L,
#> surrogatestyle = 0L, maxdepth = 30L, xval = 0L))
#>
#> attr(,"vardep.summary")
#> M R
#> 68 71
#> attr(,"class")
#> [1] "boosting"
print(learner$importance())
#> V36 V11 V28 V45 V51 V49 V40 V31
#> 8.9801282 8.4409767 7.7190910 7.6986116 7.3885210 6.1380165 5.4765157 4.4201936
#> V12 V1 V9 V4 V56 V38 V7 V43
#> 4.1382421 3.7616324 3.7238742 3.5474445 2.8526953 2.6965090 2.6274248 2.5418292
#> V57 V42 V52 V17 V29 V41 V30 V18
#> 2.5163330 2.1278691 2.1098058 1.8473137 1.7945438 1.7625285 1.1935036 0.8161955
#> V23 V20 V53 V37 V21 V10 V13 V14
#> 0.7935594 0.7810533 0.7410533 0.7298960 0.6346391 0.0000000 0.0000000 0.0000000
#> V15 V16 V19 V2 V22 V24 V25 V26
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V27 V3 V32 V33 V34 V35 V39 V44
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V46 V47 V48 V5 V50 V54 V55 V58
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V59 V6 V60 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
#> 0.2753623