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()
Examples
# Define the Learner
learner = lrn("classif.adabag", mfinal = 10L)
print(learner)
#>
#> ── <LearnerClassifAdabag> (classif.adabag): Adabag Boosting ────────────────────
#> • Model: -
#> • Parameters: mfinal=10, xval=0
#> • Packages: mlr3, adabag, and rpart
#> • Predict Types: [response] and prob
#> • Feature Types: integer, numeric, and factor
#> • Encapsulation: none (fallback: -)
#> • Properties: importance, missings, multiclass, and twoclass
#> • Other settings: use_weights = 'error', predict_raw = 'FALSE'
# Define a Task
task = tsk("sonar")
# Create train and test set
ids = partition(task)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
print(learner$model)
#> $formula
#> Class ~ .
#> NULL
#>
#> $trees
#> $trees[[1]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 64 M (0.53956835 0.46043165)
#> 2) V49>=0.03845 77 19 M (0.75324675 0.24675325)
#> 4) V31< 0.4599 31 0 M (1.00000000 0.00000000) *
#> 5) V31>=0.4599 46 19 M (0.58695652 0.41304348)
#> 10) V28>=0.66175 29 2 M (0.93103448 0.06896552) *
#> 11) V28< 0.66175 17 0 R (0.00000000 1.00000000) *
#> 3) V49< 0.03845 62 17 R (0.27419355 0.72580645)
#> 6) V11>=0.28665 17 4 M (0.76470588 0.23529412) *
#> 7) V11< 0.28665 45 4 R (0.08888889 0.91111111) *
#>
#> $trees[[2]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 64 R (0.46043165 0.53956835)
#> 2) V11>=0.13425 109 46 M (0.57798165 0.42201835)
#> 4) V16< 0.39975 48 8 M (0.83333333 0.16666667)
#> 8) V10>=0.15185 40 1 M (0.97500000 0.02500000) *
#> 9) V10< 0.15185 8 1 R (0.12500000 0.87500000) *
#> 5) V16>=0.39975 61 23 R (0.37704918 0.62295082)
#> 10) V21>=0.8191 14 0 M (1.00000000 0.00000000) *
#> 11) V21< 0.8191 47 9 R (0.19148936 0.80851064) *
#> 3) V11< 0.13425 30 1 R (0.03333333 0.96666667) *
#>
#> $trees[[3]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 57 M (0.58992806 0.41007194)
#> 2) V5>=0.0392 107 29 M (0.72897196 0.27102804)
#> 4) V4>=0.03795 76 10 M (0.86842105 0.13157895)
#> 8) V30>=0.46225 51 1 M (0.98039216 0.01960784) *
#> 9) V30< 0.46225 25 9 M (0.64000000 0.36000000)
#> 18) V27< 0.56135 16 0 M (1.00000000 0.00000000) *
#> 19) V27>=0.56135 9 0 R (0.00000000 1.00000000) *
#> 5) V4< 0.03795 31 12 R (0.38709677 0.61290323)
#> 10) V35< 0.22685 12 2 M (0.83333333 0.16666667) *
#> 11) V35>=0.22685 19 2 R (0.10526316 0.89473684) *
#> 3) V5< 0.0392 32 4 R (0.12500000 0.87500000)
#> 6) V1>=0.0228 7 3 M (0.57142857 0.42857143) *
#> 7) V1< 0.0228 25 0 R (0.00000000 1.00000000) *
#>
#> $trees[[4]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 61 M (0.56115108 0.43884892)
#> 2) V4>=0.052 52 8 M (0.84615385 0.15384615)
#> 4) V5>=0.03665 45 2 M (0.95555556 0.04444444) *
#> 5) V5< 0.03665 7 1 R (0.14285714 0.85714286) *
#> 3) V4< 0.052 87 34 R (0.39080460 0.60919540)
#> 6) V21>=0.67375 38 12 M (0.68421053 0.31578947)
#> 12) V20< 0.84235 24 0 M (1.00000000 0.00000000) *
#> 13) V20>=0.84235 14 2 R (0.14285714 0.85714286) *
#> 7) V21< 0.67375 49 8 R (0.16326531 0.83673469)
#> 14) V36< 0.15855 12 4 M (0.66666667 0.33333333) *
#> 15) V36>=0.15855 37 0 R (0.00000000 1.00000000) *
#>
#> $trees[[5]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 66 R (0.47482014 0.52517986)
#> 2) V46>=0.06695 103 40 M (0.61165049 0.38834951)
#> 4) V12>=0.1677 65 11 M (0.83076923 0.16923077)
#> 8) V27>=0.71565 44 1 M (0.97727273 0.02272727) *
#> 9) V27< 0.71565 21 10 M (0.52380952 0.47619048)
#> 18) V31< 0.4195 10 0 M (1.00000000 0.00000000) *
#> 19) V31>=0.4195 11 1 R (0.09090909 0.90909091) *
#> 5) V12< 0.1677 38 9 R (0.23684211 0.76315789)
#> 10) V25< 0.45365 11 2 M (0.81818182 0.18181818) *
#> 11) V25>=0.45365 27 0 R (0.00000000 1.00000000) *
#> 3) V46< 0.06695 36 3 R (0.08333333 0.91666667) *
#>
#> $trees[[6]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 66 R (0.47482014 0.52517986)
#> 2) V4>=0.0582 54 13 M (0.75925926 0.24074074)
#> 4) V50>=0.00645 46 6 M (0.86956522 0.13043478)
#> 8) V34>=0.1883 39 0 M (1.00000000 0.00000000) *
#> 9) V34< 0.1883 7 1 R (0.14285714 0.85714286) *
#> 5) V50< 0.00645 8 1 R (0.12500000 0.87500000) *
#> 3) V4< 0.0582 85 25 R (0.29411765 0.70588235)
#> 6) V28>=0.92035 19 5 M (0.73684211 0.26315789) *
#> 7) V28< 0.92035 66 11 R (0.16666667 0.83333333)
#> 14) V28< 0.37175 7 0 M (1.00000000 0.00000000) *
#> 15) V28>=0.37175 59 4 R (0.06779661 0.93220339) *
#>
#> $trees[[7]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 61 M (0.56115108 0.43884892)
#> 2) V21>=0.6401 79 17 M (0.78481013 0.21518987)
#> 4) V31< 0.56955 69 7 M (0.89855072 0.10144928)
#> 8) V44>=0.1384 56 0 M (1.00000000 0.00000000) *
#> 9) V44< 0.1384 13 6 R (0.46153846 0.53846154) *
#> 5) V31>=0.56955 10 0 R (0.00000000 1.00000000) *
#> 3) V21< 0.6401 60 16 R (0.26666667 0.73333333)
#> 6) V47>=0.31685 7 0 M (1.00000000 0.00000000) *
#> 7) V47< 0.31685 53 9 R (0.16981132 0.83018868)
#> 14) V36< 0.2083 8 2 M (0.75000000 0.25000000) *
#> 15) V36>=0.2083 45 3 R (0.06666667 0.93333333) *
#>
#> $trees[[8]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 62 M (0.55395683 0.44604317)
#> 2) V51>=0.01285 79 21 M (0.73417722 0.26582278)
#> 4) V36< 0.44265 53 4 M (0.92452830 0.07547170) *
#> 5) V36>=0.44265 26 9 R (0.34615385 0.65384615)
#> 10) V12>=0.24295 11 3 M (0.72727273 0.27272727) *
#> 11) V12< 0.24295 15 1 R (0.06666667 0.93333333) *
#> 3) V51< 0.01285 60 19 R (0.31666667 0.68333333)
#> 6) V44>=0.2503 12 0 M (1.00000000 0.00000000) *
#> 7) V44< 0.2503 48 7 R (0.14583333 0.85416667)
#> 14) V35< 0.2434 20 7 R (0.35000000 0.65000000)
#> 28) V52>=0.00795 8 2 M (0.75000000 0.25000000) *
#> 29) V52< 0.00795 12 1 R (0.08333333 0.91666667) *
#> 15) V35>=0.2434 28 0 R (0.00000000 1.00000000) *
#>
#> $trees[[9]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 55 M (0.60431655 0.39568345)
#> 2) V21>=0.68525 76 9 M (0.88157895 0.11842105)
#> 4) V18< 0.9179 68 4 M (0.94117647 0.05882353) *
#> 5) V18>=0.9179 8 3 R (0.37500000 0.62500000) *
#> 3) V21< 0.68525 63 17 R (0.26984127 0.73015873)
#> 6) V48>=0.0824 19 2 M (0.89473684 0.10526316) *
#> 7) V48< 0.0824 44 0 R (0.00000000 1.00000000) *
#>
#> $trees[[10]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 60 M (0.56834532 0.43165468)
#> 2) V11>=0.1168 107 29 M (0.72897196 0.27102804)
#> 4) V52>=0.0088 69 8 M (0.88405797 0.11594203)
#> 8) V12>=0.14275 62 4 M (0.93548387 0.06451613) *
#> 9) V12< 0.14275 7 3 R (0.42857143 0.57142857) *
#> 5) V52< 0.0088 38 17 R (0.44736842 0.55263158)
#> 10) V23>=0.82995 12 0 M (1.00000000 0.00000000) *
#> 11) V23< 0.82995 26 5 R (0.19230769 0.80769231)
#> 22) V43>=0.24095 7 2 M (0.71428571 0.28571429) *
#> 23) V43< 0.24095 19 0 R (0.00000000 1.00000000) *
#> 3) V11< 0.1168 32 1 R (0.03125000 0.96875000) *
#>
#>
#> $weights
#> [1] 0.7113310 0.8188278 0.6528871 0.7257103 0.9020586 0.6764200 0.9782929
#> [8] 0.6074625 0.7384970 0.9923752
#>
#> $votes
#> [,1] [,2]
#> [1,] 2.5497000 5.2541624
#> [2,] 2.6973910 5.1064714
#> [3,] 2.0223795 5.7814829
#> [4,] 2.6481006 5.1557618
#> [5,] 1.3642181 6.4396442
#> [6,] 1.4642073 6.3396551
#> [7,] 2.6188485 5.1850139
#> [8,] 2.3518866 5.4519758
#> [9,] 2.3114657 5.4923967
#> [10,] 2.0716698 5.7321925
#> [11,] 2.0223795 5.7814829
#> [12,] 1.5095211 6.2943413
#> [13,] 1.9860600 5.8178024
#> [14,] 2.3383347 5.4655277
#> [15,] 1.4149169 6.3889454
#> [16,] 0.6074625 7.1963999
#> [17,] 2.3568903 5.4469720
#> [18,] 1.7167899 6.0870725
#> [19,] 2.5473209 5.2565415
#> [20,] 0.0000000 7.8038624
#> [21,] 0.7384970 7.0653654
#> [22,] 1.3913841 6.4124783
#> [23,] 2.1027151 5.7011472
#> [24,] 3.1443758 4.6594866
#> [25,] 0.8188278 6.9850346
#> [26,] 0.0000000 7.8038624
#> [27,] 0.9020586 6.9018038
#> [28,] 3.1783163 4.6255461
#> [29,] 1.4149169 6.3889454
#> [30,] 1.9706681 5.8331943
#> [31,] 0.0000000 7.8038624
#> [32,] 0.6528871 7.1509752
#> [33,] 2.2527248 5.5511375
#> [34,] 2.0406381 5.7632243
#> [35,] 3.0283065 4.7755558
#> [36,] 0.9923752 6.8114872
#> [37,] 0.0000000 7.8038624
#> [38,] 1.6452623 6.1586001
#> [39,] 2.5369133 5.2669491
#> [40,] 1.3293071 6.4745553
#> [41,] 2.1027151 5.7011472
#> [42,] 0.7113310 7.0925314
#> [43,] 1.5998377 6.2040247
#> [44,] 0.0000000 7.8038624
#> [45,] 0.9782929 6.8255695
#> [46,] 2.2386425 5.5652199
#> [47,] 1.3459595 6.4579029
#> [48,] 2.7101776 5.0936847
#> [49,] 0.8188278 6.9850346
#> [50,] 2.9784352 4.8254272
#> [51,] 1.3785975 6.4252649
#> [52,] 1.3293071 6.4745553
#> [53,] 0.8188278 6.9850346
#> [54,] 2.1755383 5.6283241
#> [55,] 2.4422032 5.3616592
#> [56,] 2.1027151 5.7011472
#> [57,] 2.1755383 5.6283241
#> [58,] 1.3913841 6.4124783
#> [59,] 1.3187935 6.4850689
#> [60,] 0.9923752 6.8114872
#> [61,] 2.9643529 4.8395095
#> [62,] 1.7040032 6.0998592
#> [63,] 1.3459595 6.4579029
#> [64,] 5.5416870 2.2621753
#> [65,] 6.9850346 0.8188278
#> [66,] 4.6680590 3.1358033
#> [67,] 5.1046831 2.6991793
#> [68,] 6.2943413 1.5095211
#> [69,] 5.3851969 2.4186655
#> [70,] 6.9850346 0.8188278
#> [71,] 5.6904011 2.1134613
#> [72,] 6.5435127 1.2603496
#> [73,] 7.0925314 0.7113310
#> [74,] 6.4017321 1.4021303
#> [75,] 6.1904728 1.6133896
#> [76,] 6.4017321 1.4021303
#> [77,] 7.1274424 0.6764200
#> [78,] 7.8038624 0.0000000
#> [79,] 6.9850346 0.8188278
#> [80,] 7.1274424 0.6764200
#> [81,] 7.1274424 0.6764200
#> [82,] 5.3303217 2.4735406
#> [83,] 6.4017321 1.4021303
#> [84,] 5.5724967 2.2313657
#> [85,] 7.0781520 0.7257103
#> [86,] 6.9850346 0.8188278
#> [87,] 6.2040247 1.5998377
#> [88,] 6.2040247 1.5998377
#> [89,] 6.3775721 1.4262903
#> [90,] 6.2040247 1.5998377
#> [91,] 6.2943413 1.5095211
#> [92,] 6.1760935 1.6277689
#> [93,] 5.3716450 2.4322174
#> [94,] 6.1760935 1.6277689
#> [95,] 5.9926594 1.8112030
#> [96,] 6.0067417 1.7971207
#> [97,] 7.1274424 0.6764200
#> [98,] 5.2121799 2.5916825
#> [99,] 5.1978006 2.6060618
#> [100,] 4.7641825 3.0396799
#> [101,] 4.5556181 3.2482443
#> [102,] 6.8255695 0.9782929
#> [103,] 6.8255695 0.9782929
#> [104,] 5.4234392 2.3804232
#> [105,] 5.7488450 2.0550174
#> [106,] 6.1350672 1.6687951
#> [107,] 5.6208164 2.1830459
#> [108,] 5.5352066 2.2686558
#> [109,] 6.2181070 1.5857554
#> [110,] 7.1274424 0.6764200
#> [111,] 7.1274424 0.6764200
#> [112,] 5.8670928 1.9367696
#> [113,] 5.7011472 2.1027151
#> [114,] 7.8038624 0.0000000
#> [115,] 7.1274424 0.6764200
#> [116,] 7.8038624 0.0000000
#> [117,] 6.8255695 0.9782929
#> [118,] 7.8038624 0.0000000
#> [119,] 7.8038624 0.0000000
#> [120,] 6.2253838 1.5784786
#> [121,] 7.8038624 0.0000000
#> [122,] 7.1509752 0.6528871
#> [123,] 7.8038624 0.0000000
#> [124,] 7.0925314 0.7113310
#> [125,] 6.8114872 0.9923752
#> [126,] 7.0653654 0.7384970
#> [127,] 7.8038624 0.0000000
#> [128,] 7.8038624 0.0000000
#> [129,] 7.8038624 0.0000000
#> [130,] 7.8038624 0.0000000
#> [131,] 7.1509752 0.6528871
#> [132,] 7.8038624 0.0000000
#> [133,] 7.8038624 0.0000000
#> [134,] 6.9018038 0.9020586
#> [135,] 7.8038624 0.0000000
#> [136,] 6.2943413 1.5095211
#> [137,] 7.8038624 0.0000000
#> [138,] 5.4093569 2.3945055
#> [139,] 4.9519041 2.8519583
#>
#> $prob
#> [,1] [,2]
#> [1,] 0.32672283 0.67327717
#> [2,] 0.34564820 0.65435180
#> [3,] 0.25915109 0.74084891
#> [4,] 0.33933205 0.66066795
#> [5,] 0.17481320 0.82518680
#> [6,] 0.18762598 0.81237402
#> [7,] 0.33558363 0.66441637
#> [8,] 0.30137469 0.69862531
#> [9,] 0.29619509 0.70380491
#> [10,] 0.26546724 0.73453276
#> [11,] 0.25915109 0.74084891
#> [12,] 0.19343256 0.80656744
#> [13,] 0.25449705 0.74550295
#> [14,] 0.29963812 0.70036188
#> [15,] 0.18130983 0.81869017
#> [16,] 0.07784126 0.92215874
#> [17,] 0.30201588 0.69798412
#> [18,] 0.21999233 0.78000767
#> [19,] 0.32641797 0.67358203
#> [20,] 0.00000000 1.00000000
#> [21,] 0.09463224 0.90536776
#> [22,] 0.17829429 0.82170571
#> [23,] 0.26944544 0.73055456
#> [24,] 0.40292559 0.59707441
#> [25,] 0.10492597 0.89507403
#> [26,] 0.00000000 1.00000000
#> [27,] 0.11559130 0.88440870
#> [28,] 0.40727477 0.59272523
#> [29,] 0.18130983 0.81869017
#> [30,] 0.25252471 0.74747529
#> [31,] 0.00000000 1.00000000
#> [32,] 0.08366205 0.91633795
#> [33,] 0.28866793 0.71133207
#> [34,] 0.26149078 0.73850922
#> [35,] 0.38805227 0.61194773
#> [36,] 0.12716462 0.87283538
#> [37,] 0.00000000 1.00000000
#> [38,] 0.21082667 0.78917333
#> [39,] 0.32508432 0.67491568
#> [40,] 0.17033964 0.82966036
#> [41,] 0.26944544 0.73055456
#> [42,] 0.09115115 0.90884885
#> [43,] 0.20500588 0.79499412
#> [44,] 0.00000000 1.00000000
#> [45,] 0.12536009 0.87463991
#> [46,] 0.28686340 0.71313660
#> [47,] 0.17247351 0.82752649
#> [48,] 0.34728670 0.65271330
#> [49,] 0.10492597 0.89507403
#> [50,] 0.38166167 0.61833833
#> [51,] 0.17665579 0.82334421
#> [52,] 0.17033964 0.82966036
#> [53,] 0.10492597 0.89507403
#> [54,] 0.27877713 0.72122287
#> [55,] 0.31294801 0.68705199
#> [56,] 0.26944544 0.73055456
#> [57,] 0.27877713 0.72122287
#> [58,] 0.17829429 0.82170571
#> [59,] 0.16899241 0.83100759
#> [60,] 0.12716462 0.87283538
#> [61,] 0.37985714 0.62014286
#> [62,] 0.21835383 0.78164617
#> [63,] 0.17247351 0.82752649
#> [64,] 0.71012106 0.28987894
#> [65,] 0.89507403 0.10492597
#> [66,] 0.59817291 0.40182709
#> [67,] 0.65412265 0.34587735
#> [68,] 0.80656744 0.19343256
#> [69,] 0.69006815 0.30993185
#> [70,] 0.89507403 0.10492597
#> [71,] 0.72917753 0.27082247
#> [72,] 0.83849668 0.16150332
#> [73,] 0.90884885 0.09115115
#> [74,] 0.82032868 0.17967132
#> [75,] 0.79325755 0.20674245
#> [76,] 0.82032868 0.17967132
#> [77,] 0.91332241 0.08667759
#> [78,] 1.00000000 0.00000000
#> [79,] 0.89507403 0.10492597
#> [80,] 0.91332241 0.08667759
#> [81,] 0.91332241 0.08667759
#> [82,] 0.68303636 0.31696364
#> [83,] 0.82032868 0.17967132
#> [84,] 0.71406906 0.28593094
#> [85,] 0.90700626 0.09299374
#> [86,] 0.89507403 0.10492597
#> [87,] 0.79499412 0.20500588
#> [88,] 0.79499412 0.20500588
#> [89,] 0.81723277 0.18276723
#> [90,] 0.79499412 0.20500588
#> [91,] 0.80656744 0.19343256
#> [92,] 0.79141496 0.20858504
#> [93,] 0.68833159 0.31166841
#> [94,] 0.79141496 0.20858504
#> [95,] 0.76790942 0.23209058
#> [96,] 0.76971395 0.23028605
#> [97,] 0.91332241 0.08667759
#> [98,] 0.66789747 0.33210253
#> [99,] 0.66605487 0.33394513
#> [100,] 0.61049032 0.38950968
#> [101,] 0.58376453 0.41623547
#> [102,] 0.87463991 0.12536009
#> [103,] 0.87463991 0.12536009
#> [104,] 0.69496859 0.30503141
#> [105,] 0.73666662 0.26333338
#> [106,] 0.78615779 0.21384221
#> [107,] 0.72026084 0.27973916
#> [108,] 0.70929065 0.29070935
#> [109,] 0.79679865 0.20320135
#> [110,] 0.91332241 0.08667759
#> [111,] 0.91332241 0.08667759
#> [112,] 0.75181910 0.24818090
#> [113,] 0.73055456 0.26944544
#> [114,] 1.00000000 0.00000000
#> [115,] 0.91332241 0.08667759
#> [116,] 1.00000000 0.00000000
#> [117,] 0.87463991 0.12536009
#> [118,] 1.00000000 0.00000000
#> [119,] 1.00000000 0.00000000
#> [120,] 0.79773111 0.20226889
#> [121,] 1.00000000 0.00000000
#> [122,] 0.91633795 0.08366205
#> [123,] 1.00000000 0.00000000
#> [124,] 0.90884885 0.09115115
#> [125,] 0.87283538 0.12716462
#> [126,] 0.90536776 0.09463224
#> [127,] 1.00000000 0.00000000
#> [128,] 1.00000000 0.00000000
#> [129,] 1.00000000 0.00000000
#> [130,] 1.00000000 0.00000000
#> [131,] 0.91633795 0.08366205
#> [132,] 1.00000000 0.00000000
#> [133,] 1.00000000 0.00000000
#> [134,] 0.88440870 0.11559130
#> [135,] 1.00000000 0.00000000
#> [136,] 0.80656744 0.19343256
#> [137,] 1.00000000 0.00000000
#> [138,] 0.69316406 0.30683594
#> [139,] 0.63454529 0.36545471
#>
#> $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" "M" "M" "M" "M" "M" "M" "M" "M" "M"
#> [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
#> [91] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
#> [109] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
#> [127] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
#>
#> $importance
#> V1 V10 V11 V12 V13 V14 V15
#> 0.5675397 1.9199299 10.5260890 5.3142941 0.0000000 0.0000000 0.0000000
#> V16 V17 V18 V19 V2 V20 V21
#> 2.2292261 0.0000000 0.8248669 0.0000000 0.0000000 2.2949414 13.8565511
#> V22 V23 V24 V25 V26 V27 V28
#> 0.0000000 2.5875275 0.0000000 2.2974890 0.0000000 3.1142383 6.5867490
#> V29 V3 V30 V31 V32 V33 V34
#> 0.0000000 0.0000000 0.6177887 6.3533168 0.0000000 0.0000000 1.4357335
#> V35 V36 V37 V38 V39 V4 V40
#> 1.6617117 4.6586182 0.0000000 0.0000000 0.0000000 6.3577634 0.0000000
#> V41 V42 V43 V44 V45 V46 V47
#> 0.0000000 0.0000000 1.2607945 3.5280234 0.0000000 3.2696050 2.0295108
#> V48 V49 V5 V50 V51 V52 V53
#> 3.8190144 2.7293111 4.2692934 1.2440051 1.7577784 2.8882895 0.0000000
#> V54 V55 V56 V57 V58 V59 V6
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V60 V7 V8 V9
#> 0.0000000 0.0000000 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"
#>
#> $call
#> adabag::boosting(formula = formula, data = data, mfinal = 10L,
#> control = list(minsplit = 20L, minbucket = 7, cp = 0.01,
#> maxcompete = 4L, maxsurrogate = 5L, usesurrogate = 2L,
#> surrogatestyle = 0L, maxdepth = 30L, xval = 0L))
#>
#> attr(,"vardep.summary")
#> M R
#> 76 63
#> attr(,"class")
#> [1] "boosting"
print(learner$importance())
#> V21 V11 V28 V4 V31 V12 V36
#> 13.8565511 10.5260890 6.5867490 6.3577634 6.3533168 5.3142941 4.6586182
#> V5 V48 V44 V46 V27 V52 V49
#> 4.2692934 3.8190144 3.5280234 3.2696050 3.1142383 2.8882895 2.7293111
#> V23 V25 V20 V16 V47 V10 V51
#> 2.5875275 2.2974890 2.2949414 2.2292261 2.0295108 1.9199299 1.7577784
#> V35 V34 V43 V50 V18 V30 V1
#> 1.6617117 1.4357335 1.2607945 1.2440051 0.8248669 0.6177887 0.5675397
#> V13 V14 V15 V17 V19 V2 V22
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V24 V26 V29 V3 V32 V33 V37
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V38 V39 V40 V41 V42 V45 V53
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V54 V55 V56 V57 V58 V59 V6
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V60 V7 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
#> 0.1884058