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 66 M (0.52517986 0.47482014)
#> 2) V9>=0.10375 93 28 M (0.69892473 0.30107527)
#> 4) V17< 0.7654 80 17 M (0.78750000 0.21250000)
#> 8) V26>=0.2795 72 9 M (0.87500000 0.12500000)
#> 16) V49>=0.0231 65 4 M (0.93846154 0.06153846) *
#> 17) V49< 0.0231 7 2 R (0.28571429 0.71428571) *
#> 9) V26< 0.2795 8 0 R (0.00000000 1.00000000) *
#> 5) V17>=0.7654 13 2 R (0.15384615 0.84615385) *
#> 3) V9< 0.10375 46 8 R (0.17391304 0.82608696)
#> 6) V4>=0.07835 7 1 M (0.85714286 0.14285714) *
#> 7) V4< 0.07835 39 2 R (0.05128205 0.94871795) *
#>
#> $trees[[2]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 59 M (0.57553957 0.42446043)
#> 2) V51>=0.0126 86 19 M (0.77906977 0.22093023)
#> 4) V11>=0.17885 59 4 M (0.93220339 0.06779661) *
#> 5) V11< 0.17885 27 12 R (0.44444444 0.55555556)
#> 10) V60< 0.00685 15 3 M (0.80000000 0.20000000) *
#> 11) V60>=0.00685 12 0 R (0.00000000 1.00000000) *
#> 3) V51< 0.0126 53 13 R (0.24528302 0.75471698)
#> 6) V4>=0.05195 14 4 M (0.71428571 0.28571429) *
#> 7) V4< 0.05195 39 3 R (0.07692308 0.92307692) *
#>
#> $trees[[3]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 60 R (0.43165468 0.56834532)
#> 2) V39>=0.35865 43 11 M (0.74418605 0.25581395)
#> 4) V40< 0.5831 35 4 M (0.88571429 0.11428571) *
#> 5) V40>=0.5831 8 1 R (0.12500000 0.87500000) *
#> 3) V39< 0.35865 96 28 R (0.29166667 0.70833333)
#> 6) V23>=0.785 38 17 M (0.55263158 0.44736842)
#> 12) V32>=0.2409 17 1 M (0.94117647 0.05882353) *
#> 13) V32< 0.2409 21 5 R (0.23809524 0.76190476)
#> 26) V22>=0.91055 7 2 M (0.71428571 0.28571429) *
#> 27) V22< 0.91055 14 0 R (0.00000000 1.00000000) *
#> 7) V23< 0.785 58 7 R (0.12068966 0.87931034)
#> 14) V28>=0.9233 11 5 M (0.54545455 0.45454545) *
#> 15) V28< 0.9233 47 1 R (0.02127660 0.97872340) *
#>
#> $trees[[4]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 58 R (0.41726619 0.58273381)
#> 2) V48>=0.0766 67 22 M (0.67164179 0.32835821)
#> 4) V17< 0.51 42 5 M (0.88095238 0.11904762)
#> 8) V11>=0.10235 33 0 M (1.00000000 0.00000000) *
#> 9) V11< 0.10235 9 4 R (0.44444444 0.55555556) *
#> 5) V17>=0.51 25 8 R (0.32000000 0.68000000)
#> 10) V22>=0.98585 7 0 M (1.00000000 0.00000000) *
#> 11) V22< 0.98585 18 1 R (0.05555556 0.94444444) *
#> 3) V48< 0.0766 72 13 R (0.18055556 0.81944444)
#> 6) V19>=0.89675 10 3 M (0.70000000 0.30000000) *
#> 7) V19< 0.89675 62 6 R (0.09677419 0.90322581) *
#>
#> $trees[[5]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 69 R (0.49640288 0.50359712)
#> 2) V52>=0.0077 101 33 M (0.67326733 0.32673267)
#> 4) V45>=0.1269 62 8 M (0.87096774 0.12903226) *
#> 5) V45< 0.1269 39 14 R (0.35897436 0.64102564)
#> 10) V10>=0.1624 16 3 M (0.81250000 0.18750000) *
#> 11) V10< 0.1624 23 1 R (0.04347826 0.95652174) *
#> 3) V52< 0.0077 38 1 R (0.02631579 0.97368421) *
#>
#> $trees[[6]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 64 R (0.46043165 0.53956835)
#> 2) V27>=0.878 45 9 M (0.80000000 0.20000000)
#> 4) V12>=0.18145 36 2 M (0.94444444 0.05555556) *
#> 5) V12< 0.18145 9 2 R (0.22222222 0.77777778) *
#> 3) V27< 0.878 94 28 R (0.29787234 0.70212766)
#> 6) V21>=0.76785 27 9 M (0.66666667 0.33333333)
#> 12) V31< 0.37445 19 2 M (0.89473684 0.10526316) *
#> 13) V31>=0.37445 8 1 R (0.12500000 0.87500000) *
#> 7) V21< 0.76785 67 10 R (0.14925373 0.85074627)
#> 14) V44>=0.2637 14 5 M (0.64285714 0.35714286) *
#> 15) V44< 0.2637 53 1 R (0.01886792 0.98113208) *
#>
#> $trees[[7]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 69 R (0.49640288 0.50359712)
#> 2) V5>=0.05935 81 25 M (0.69135802 0.30864198)
#> 4) V47>=0.0614 67 12 M (0.82089552 0.17910448)
#> 8) V37< 0.4718 45 2 M (0.95555556 0.04444444) *
#> 9) V37>=0.4718 22 10 M (0.54545455 0.45454545)
#> 18) V1>=0.0428 10 0 M (1.00000000 0.00000000) *
#> 19) V1< 0.0428 12 2 R (0.16666667 0.83333333) *
#> 5) V47< 0.0614 14 1 R (0.07142857 0.92857143) *
#> 3) V5< 0.05935 58 13 R (0.22413793 0.77586207)
#> 6) V49>=0.07415 9 2 M (0.77777778 0.22222222) *
#> 7) V49< 0.07415 49 6 R (0.12244898 0.87755102) *
#>
#> $trees[[8]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 57 R (0.41007194 0.58992806)
#> 2) V45>=0.2393 30 4 M (0.86666667 0.13333333)
#> 4) V11>=0.2153 23 0 M (1.00000000 0.00000000) *
#> 5) V11< 0.2153 7 3 R (0.42857143 0.57142857) *
#> 3) V45< 0.2393 109 31 R (0.28440367 0.71559633)
#> 6) V36< 0.40695 55 27 R (0.49090909 0.50909091)
#> 12) V43>=0.2346 17 2 M (0.88235294 0.11764706) *
#> 13) V43< 0.2346 38 12 R (0.31578947 0.68421053)
#> 26) V32>=0.23745 25 12 R (0.48000000 0.52000000)
#> 52) V50< 0.01585 15 4 M (0.73333333 0.26666667) *
#> 53) V50>=0.01585 10 1 R (0.10000000 0.90000000) *
#> 27) V32< 0.23745 13 0 R (0.00000000 1.00000000) *
#> 7) V36>=0.40695 54 4 R (0.07407407 0.92592593) *
#>
#> $trees[[9]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 68 R (0.48920863 0.51079137)
#> 2) V59>=0.012 24 2 M (0.91666667 0.08333333) *
#> 3) V59< 0.012 115 46 R (0.40000000 0.60000000)
#> 6) V59< 0.0084 80 37 M (0.53750000 0.46250000)
#> 12) V9>=0.11155 50 12 M (0.76000000 0.24000000)
#> 24) V4< 0.0821 43 6 M (0.86046512 0.13953488) *
#> 25) V4>=0.0821 7 1 R (0.14285714 0.85714286) *
#> 13) V9< 0.11155 30 5 R (0.16666667 0.83333333)
#> 26) V9< 0.0611 9 4 M (0.55555556 0.44444444) *
#> 27) V9>=0.0611 21 0 R (0.00000000 1.00000000) *
#> 7) V59>=0.0084 35 3 R (0.08571429 0.91428571) *
#>
#> $trees[[10]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 62 R (0.44604317 0.55395683)
#> 2) V45>=0.2383 35 0 M (1.00000000 0.00000000) *
#> 3) V45< 0.2383 104 27 R (0.25961538 0.74038462)
#> 6) V54>=0.0218 9 0 M (1.00000000 0.00000000) *
#> 7) V54< 0.0218 95 18 R (0.18947368 0.81052632)
#> 14) V21>=0.88775 16 6 M (0.62500000 0.37500000) *
#> 15) V21< 0.88775 79 8 R (0.10126582 0.89873418)
#> 30) V28>=0.958 7 3 M (0.57142857 0.42857143) *
#> 31) V28< 0.958 72 4 R (0.05555556 0.94444444) *
#>
#>
#> $weights
#> [1] 0.9215264 0.6044373 0.6641745 0.7762386 0.6215099 0.8493458 0.7137240
#> [8] 0.7564254 0.8322078 0.7166808
#>
#> $votes
#> [,1] [,2]
#> [1,] 2.0581550 5.3981154
#> [2,] 2.6465470 4.8097234
#> [3,] 1.9852925 5.4709779
#> [4,] 2.2528077 5.2034627
#> [5,] 1.5430363 5.9132341
#> [6,] 2.2066434 5.2496270
#> [7,] 0.6641745 6.7920959
#> [8,] 1.4963823 5.9598881
#> [9,] 2.8346384 4.6216320
#> [10,] 2.0581550 5.3981154
#> [11,] 0.0000000 7.4562704
#> [12,] 2.7919738 4.6642966
#> [13,] 1.8901216 5.5661488
#> [14,] 2.0752930 5.3809774
#> [15,] 2.9637751 4.4924953
#> [16,] 0.6044373 6.8518331
#> [17,] 2.3022022 5.1540682
#> [18,] 1.3181612 6.1381092
#> [19,] 2.2597171 5.1965533
#> [20,] 0.6215099 6.8347605
#> [21,] 0.6641745 6.7920959
#> [22,] 1.5259636 5.9303068
#> [23,] 0.6044373 6.8518331
#> [24,] 1.4963823 5.9598881
#> [25,] 0.6641745 6.7920959
#> [26,] 0.7564254 6.6998450
#> [27,] 0.7137240 6.7425464
#> [28,] 1.4537177 6.0025527
#> [29,] 0.8322078 6.6240626
#> [30,] 2.0023652 5.4539052
#> [31,] 2.7394674 4.7168030
#> [32,] 2.2859909 5.1702795
#> [33,] 2.3422652 5.1140052
#> [34,] 2.1008195 5.3554509
#> [35,] 0.7564254 6.6998450
#> [36,] 1.3608627 6.0954077
#> [37,] 3.1345895 4.3216809
#> [38,] 3.0461843 4.4100861
#> [39,] 1.4963823 5.9598881
#> [40,] 0.6641745 6.7920959
#> [41,] 2.2130631 5.2432073
#> [42,] 0.0000000 7.4562704
#> [43,] 1.4366451 6.0196253
#> [44,] 0.8322078 6.6240626
#> [45,] 0.0000000 7.4562704
#> [46,] 1.4537177 6.0025527
#> [47,] 0.9215264 6.5347440
#> [48,] 1.7537342 5.7025362
#> [49,] 0.7564254 6.6998450
#> [50,] 2.2426445 5.2136259
#> [51,] 1.4929194 5.9633510
#> [52,] 2.1674417 5.2888287
#> [53,] 0.8322078 6.6240626
#> [54,] 1.5459318 5.9103386
#> [55,] 1.5459318 5.9103386
#> [56,] 1.2259471 6.2303233
#> [57,] 1.9396711 5.5165993
#> [58,] 2.0581550 5.3981154
#> [59,] 2.8611975 4.5950729
#> [60,] 2.1350302 5.3212402
#> [61,] 1.4537177 6.0025527
#> [62,] 1.4537177 6.0025527
#> [63,] 1.4205999 6.0356705
#> [64,] 2.1008195 5.3554509
#> [65,] 0.6641745 6.7920959
#> [66,] 4.6024883 2.8537821
#> [67,] 5.2260693 2.2302011
#> [68,] 5.1995102 2.2567602
#> [69,] 4.3974633 3.0588071
#> [70,] 5.4043557 2.0519147
#> [71,] 5.2687338 2.1875365
#> [72,] 6.6800318 0.7762386
#> [73,] 5.9861210 1.4701494
#> [74,] 6.6998450 0.7564254
#> [75,] 6.0954077 1.3608627
#> [76,] 5.7747168 1.6815536
#> [77,] 5.3447980 2.1114724
#> [78,] 5.8676372 1.5886332
#> [79,] 5.8478240 1.6084464
#> [80,] 5.9236064 1.5326640
#> [81,] 5.9236064 1.5326640
#> [82,] 6.0585219 1.3977485
#> [83,] 4.9262976 2.5299728
#> [84,] 5.7585054 1.6977650
#> [85,] 6.0585219 1.3977485
#> [86,] 6.5347440 0.9215264
#> [87,] 6.7920959 0.6641745
#> [88,] 4.9984782 2.4577922
#> [89,] 7.4562704 0.0000000
#> [90,] 6.6069246 0.8493458
#> [91,] 5.1338183 2.3224521
#> [92,] 5.2289893 2.2272811
#> [93,] 6.7920959 0.6641745
#> [94,] 6.7425464 0.7137240
#> [95,] 6.7920959 0.6641745
#> [96,] 4.4272241 3.0290463
#> [97,] 7.4562704 0.0000000
#> [98,] 6.7920959 0.6641745
#> [99,] 6.0783351 1.3779353
#> [100,] 5.0447815 2.4114889
#> [101,] 5.9861210 1.4701494
#> [102,] 5.7783186 1.6779518
#> [103,] 6.0585219 1.3977485
#> [104,] 6.6069246 0.8493458
#> [105,] 6.7425464 0.7137240
#> [106,] 6.0783720 1.3778984
#> [107,] 6.7425464 0.7137240
#> [108,] 6.7425464 0.7137240
#> [109,] 6.7425464 0.7137240
#> [110,] 6.0754151 1.3808553
#> [111,] 5.1338183 2.3224521
#> [112,] 5.2034627 2.2528077
#> [113,] 4.9289728 2.5272976
#> [114,] 4.6054452 2.8508252
#> [115,] 4.4403442 3.0159262
#> [116,] 5.2694402 2.1868302
#> [117,] 5.9633510 1.4929194
#> [118,] 5.3447980 2.1114724
#> [119,] 6.1381092 1.3181612
#> [120,] 5.2260693 2.2302011
#> [121,] 6.6069246 0.8493458
#> [122,] 7.4562704 0.0000000
#> [123,] 7.4562704 0.0000000
#> [124,] 6.8518331 0.6044373
#> [125,] 5.8932006 1.5630698
#> [126,] 6.6069246 0.8493458
#> [127,] 5.9103386 1.5459318
#> [128,] 6.8347605 0.6215099
#> [129,] 6.8347605 0.6215099
#> [130,] 5.9861210 1.4701494
#> [131,] 6.1381092 1.3181612
#> [132,] 7.4562704 0.0000000
#> [133,] 5.9831641 1.4731062
#> [134,] 5.9831641 1.4731062
#> [135,] 6.7425464 0.7137240
#> [136,] 5.8676372 1.5886332
#> [137,] 6.6998450 0.7564254
#> [138,] 5.9861210 1.4701494
#> [139,] 4.3281006 3.1281697
#>
#> $prob
#> [,1] [,2]
#> [1,] 0.27603009 0.72396991
#> [2,] 0.35494247 0.64505753
#> [3,] 0.26625812 0.73374188
#> [4,] 0.30213600 0.69786400
#> [5,] 0.20694478 0.79305522
#> [6,] 0.29594465 0.70405535
#> [7,] 0.08907596 0.91092404
#> [8,] 0.20068777 0.79931223
#> [9,] 0.38016840 0.61983160
#> [10,] 0.27603009 0.72396991
#> [11,] 0.00000000 1.00000000
#> [12,] 0.37444643 0.62555357
#> [13,] 0.25349424 0.74650576
#> [14,] 0.27832856 0.72167144
#> [15,] 0.39748761 0.60251239
#> [16,] 0.08106429 0.91893571
#> [17,] 0.30876056 0.69123944
#> [18,] 0.17678560 0.82321440
#> [19,] 0.30306266 0.69693734
#> [20,] 0.08335399 0.91664601
#> [21,] 0.08907596 0.91092404
#> [22,] 0.20465508 0.79534492
#> [23,] 0.08106429 0.91893571
#> [24,] 0.20068777 0.79931223
#> [25,] 0.08907596 0.91092404
#> [26,] 0.10144823 0.89855177
#> [27,] 0.09572131 0.90427869
#> [28,] 0.19496580 0.80503420
#> [29,] 0.11161181 0.88838819
#> [30,] 0.26854782 0.73145218
#> [31,] 0.36740452 0.63259548
#> [32,] 0.30658637 0.69341363
#> [33,] 0.31413362 0.68586638
#> [34,] 0.28175206 0.71824794
#> [35,] 0.10144823 0.89855177
#> [36,] 0.18251252 0.81748748
#> [37,] 0.42039643 0.57960357
#> [38,] 0.40853994 0.59146006
#> [39,] 0.20068777 0.79931223
#> [40,] 0.08907596 0.91092404
#> [41,] 0.29680564 0.70319436
#> [42,] 0.00000000 1.00000000
#> [43,] 0.19267610 0.80732390
#> [44,] 0.11161181 0.88838819
#> [45,] 0.00000000 1.00000000
#> [46,] 0.19496580 0.80503420
#> [47,] 0.12359079 0.87640921
#> [48,] 0.23520260 0.76479740
#> [49,] 0.10144823 0.89855177
#> [50,] 0.30077295 0.69922705
#> [51,] 0.20022335 0.79977665
#> [52,] 0.29068711 0.70931289
#> [53,] 0.11161181 0.88838819
#> [54,] 0.20733312 0.79266688
#> [55,] 0.20733312 0.79266688
#> [56,] 0.16441828 0.83558172
#> [57,] 0.26013959 0.73986041
#> [58,] 0.27603009 0.72396991
#> [59,] 0.38373038 0.61626962
#> [60,] 0.28634023 0.71365977
#> [61,] 0.19496580 0.80503420
#> [62,] 0.19496580 0.80503420
#> [63,] 0.19052419 0.80947581
#> [64,] 0.28175206 0.71824794
#> [65,] 0.08907596 0.91092404
#> [66,] 0.61726414 0.38273586
#> [67,] 0.70089589 0.29910411
#> [68,] 0.69733391 0.30266609
#> [69,] 0.58976715 0.41023285
#> [70,] 0.72480683 0.27519317
#> [71,] 0.70661786 0.29338214
#> [72,] 0.89589452 0.10410548
#> [73,] 0.80283046 0.19716954
#> [74,] 0.89855177 0.10144823
#> [75,] 0.81748748 0.18251252
#> [76,] 0.77447792 0.22552208
#> [77,] 0.71681923 0.28318077
#> [78,] 0.78693997 0.21306003
#> [79,] 0.78428272 0.21571728
#> [80,] 0.79444630 0.20555370
#> [81,] 0.79444630 0.20555370
#> [82,] 0.81254053 0.18745947
#> [83,] 0.66069192 0.33930808
#> [84,] 0.77230373 0.22769627
#> [85,] 0.81254053 0.18745947
#> [86,] 0.87640921 0.12359079
#> [87,] 0.91092404 0.08907596
#> [88,] 0.67037244 0.32962756
#> [89,] 1.00000000 0.00000000
#> [90,] 0.88608972 0.11391028
#> [91,] 0.68852362 0.31147638
#> [92,] 0.70128751 0.29871249
#> [93,] 0.91092404 0.08907596
#> [94,] 0.90427869 0.09572131
#> [95,] 0.91092404 0.08907596
#> [96,] 0.59375853 0.40624147
#> [97,] 1.00000000 0.00000000
#> [98,] 0.91092404 0.08907596
#> [99,] 0.81519778 0.18480222
#> [100,] 0.67658242 0.32341758
#> [101,] 0.80283046 0.19716954
#> [102,] 0.77496098 0.22503902
#> [103,] 0.81254053 0.18745947
#> [104,] 0.88608972 0.11391028
#> [105,] 0.90427869 0.09572131
#> [106,] 0.81520273 0.18479727
#> [107,] 0.90427869 0.09572131
#> [108,] 0.90427869 0.09572131
#> [109,] 0.90427869 0.09572131
#> [110,] 0.81480617 0.18519383
#> [111,] 0.68852362 0.31147638
#> [112,] 0.69786400 0.30213600
#> [113,] 0.66105070 0.33894930
#> [114,] 0.61766070 0.38233930
#> [115,] 0.59551813 0.40448187
#> [116,] 0.70671259 0.29328741
#> [117,] 0.79977665 0.20022335
#> [118,] 0.71681923 0.28318077
#> [119,] 0.82321440 0.17678560
#> [120,] 0.70089589 0.29910411
#> [121,] 0.88608972 0.11391028
#> [122,] 1.00000000 0.00000000
#> [123,] 1.00000000 0.00000000
#> [124,] 0.91893571 0.08106429
#> [125,] 0.79036841 0.20963159
#> [126,] 0.88608972 0.11391028
#> [127,] 0.79266688 0.20733312
#> [128,] 0.91664601 0.08335399
#> [129,] 0.91664601 0.08335399
#> [130,] 0.80283046 0.19716954
#> [131,] 0.82321440 0.17678560
#> [132,] 1.00000000 0.00000000
#> [133,] 0.80243390 0.19756610
#> [134,] 0.80243390 0.19756610
#> [135,] 0.90427869 0.09572131
#> [136,] 0.78693997 0.21306003
#> [137,] 0.89855177 0.10144823
#> [138,] 0.80283046 0.19716954
#> [139,] 0.58046455 0.41953545
#>
#> $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 V16
#> 1.542345 1.978625 3.242363 1.819759 0.000000 0.000000 0.000000 0.000000
#> V17 V18 V19 V2 V20 V21 V22 V23
#> 4.544393 0.000000 1.387616 0.000000 0.000000 3.988659 2.892997 1.623037
#> V24 V25 V26 V27 V28 V29 V3 V30
#> 0.000000 0.000000 2.898087 3.717864 1.622197 0.000000 0.000000 0.000000
#> V31 V32 V33 V34 V35 V36 V37 V38
#> 1.616233 2.610038 0.000000 0.000000 0.000000 2.043053 1.011876 0.000000
#> V39 V4 V40 V41 V42 V43 V44 V45
#> 2.304287 4.941156 1.427809 0.000000 0.000000 1.627002 2.089407 11.536288
#> V46 V47 V48 V49 V5 V50 V51 V52
#> 0.000000 2.648550 3.706452 2.745152 3.004185 1.038573 3.221821 4.097669
#> V53 V54 V55 V56 V57 V58 V59 V6
#> 0.000000 2.208248 0.000000 0.000000 0.000000 0.000000 4.875987 0.000000
#> V60 V7 V8 V9
#> 1.471279 0.000000 0.000000 8.516993
#>
#> $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())
#> V45 V9 V4 V59 V17 V52 V21 V27
#> 11.536288 8.516993 4.941156 4.875987 4.544393 4.097669 3.988659 3.717864
#> V48 V11 V51 V5 V26 V22 V49 V47
#> 3.706452 3.242363 3.221821 3.004185 2.898087 2.892997 2.745152 2.648550
#> V32 V39 V54 V44 V36 V10 V12 V43
#> 2.610038 2.304287 2.208248 2.089407 2.043053 1.978625 1.819759 1.627002
#> V23 V28 V31 V1 V60 V40 V19 V50
#> 1.623037 1.622197 1.616233 1.542345 1.471279 1.427809 1.387616 1.038573
#> V37 V13 V14 V15 V16 V18 V2 V20
#> 1.011876 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> V24 V25 V29 V3 V30 V33 V34 V35
#> 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> V38 V41 V42 V46 V53 V55 V56 V57
#> 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
#> V58 V6 V7 V8
#> 0.000000 0.000000 0.000000 0.000000
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.1884058