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 67 M (0.51798561 0.48201439)
#> 2) V49>=0.04575 76 15 M (0.80263158 0.19736842)
#> 4) V12>=0.1941 56 2 M (0.96428571 0.03571429) *
#> 5) V12< 0.1941 20 7 R (0.35000000 0.65000000)
#> 10) V20>=0.4866 7 0 M (1.00000000 0.00000000) *
#> 11) V20< 0.4866 13 0 R (0.00000000 1.00000000) *
#> 3) V49< 0.04575 63 11 R (0.17460317 0.82539683)
#> 6) V31< 0.4072 21 10 R (0.47619048 0.52380952)
#> 12) V60>=0.008 7 0 M (1.00000000 0.00000000) *
#> 13) V60< 0.008 14 3 R (0.21428571 0.78571429) *
#> 7) V31>=0.4072 42 1 R (0.02380952 0.97619048) *
#>
#> $trees[[2]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 56 M (0.59712230 0.40287770)
#> 2) V11>=0.18875 92 23 M (0.75000000 0.25000000)
#> 4) V27>=0.8191 45 0 M (1.00000000 0.00000000) *
#> 5) V27< 0.8191 47 23 M (0.51063830 0.48936170)
#> 10) V56>=0.00625 29 6 M (0.79310345 0.20689655)
#> 20) V21>=0.6543 22 1 M (0.95454545 0.04545455) *
#> 21) V21< 0.6543 7 2 R (0.28571429 0.71428571) *
#> 11) V56< 0.00625 18 1 R (0.05555556 0.94444444) *
#> 3) V11< 0.18875 47 14 R (0.29787234 0.70212766)
#> 6) V16>=0.3645 16 3 M (0.81250000 0.18750000) *
#> 7) V16< 0.3645 31 1 R (0.03225806 0.96774194) *
#>
#> $trees[[3]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 59 M (0.57553957 0.42446043)
#> 2) V11>=0.19615 91 20 M (0.78021978 0.21978022)
#> 4) V51>=0.01285 59 2 M (0.96610169 0.03389831) *
#> 5) V51< 0.01285 32 14 R (0.43750000 0.56250000)
#> 10) V33>=0.2395 18 4 M (0.77777778 0.22222222) *
#> 11) V33< 0.2395 14 0 R (0.00000000 1.00000000) *
#> 3) V11< 0.19615 48 9 R (0.18750000 0.81250000)
#> 6) V59>=0.01035 9 2 M (0.77777778 0.22222222) *
#> 7) V59< 0.01035 39 2 R (0.05128205 0.94871795) *
#>
#> $trees[[4]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 65 M (0.53237410 0.46762590)
#> 2) V35< 0.2208 46 4 M (0.91304348 0.08695652) *
#> 3) V35>=0.2208 93 32 R (0.34408602 0.65591398)
#> 6) V44>=0.3511 11 0 M (1.00000000 0.00000000) *
#> 7) V44< 0.3511 82 21 R (0.25609756 0.74390244)
#> 14) V23>=0.7803 29 10 M (0.65517241 0.34482759)
#> 28) V37< 0.4651 21 2 M (0.90476190 0.09523810) *
#> 29) V37>=0.4651 8 0 R (0.00000000 1.00000000) *
#> 15) V23< 0.7803 53 2 R (0.03773585 0.96226415) *
#>
#> $trees[[5]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 67 R (0.48201439 0.51798561)
#> 2) V52>=0.01405 53 11 M (0.79245283 0.20754717)
#> 4) V32< 0.49425 39 1 M (0.97435897 0.02564103) *
#> 5) V32>=0.49425 14 4 R (0.28571429 0.71428571) *
#> 3) V52< 0.01405 86 25 R (0.29069767 0.70930233)
#> 6) V21>=0.65735 44 22 M (0.50000000 0.50000000)
#> 12) V1>=0.0128 28 6 M (0.78571429 0.21428571)
#> 24) V15< 0.5467 21 0 M (1.00000000 0.00000000) *
#> 25) V15>=0.5467 7 1 R (0.14285714 0.85714286) *
#> 13) V1< 0.0128 16 0 R (0.00000000 1.00000000) *
#> 7) V21< 0.65735 42 3 R (0.07142857 0.92857143) *
#>
#> $trees[[6]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 65 M (0.53237410 0.46762590)
#> 2) V9>=0.1645 73 15 M (0.79452055 0.20547945)
#> 4) V52>=0.0047 65 8 M (0.87692308 0.12307692) *
#> 5) V52< 0.0047 8 1 R (0.12500000 0.87500000) *
#> 3) V9< 0.1645 66 16 R (0.24242424 0.75757576)
#> 6) V32< 0.3314 23 9 M (0.60869565 0.39130435)
#> 12) V44>=0.1862 12 0 M (1.00000000 0.00000000) *
#> 13) V44< 0.1862 11 2 R (0.18181818 0.81818182) *
#> 7) V32>=0.3314 43 2 R (0.04651163 0.95348837) *
#>
#> $trees[[7]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 64 R (0.46043165 0.53956835)
#> 2) V39>=0.1744 101 40 M (0.60396040 0.39603960)
#> 4) V50>=0.01785 41 5 M (0.87804878 0.12195122)
#> 8) V10>=0.09335 34 1 M (0.97058824 0.02941176) *
#> 9) V10< 0.09335 7 3 R (0.42857143 0.57142857) *
#> 5) V50< 0.01785 60 25 R (0.41666667 0.58333333)
#> 10) V42>=0.29755 19 2 M (0.89473684 0.10526316) *
#> 11) V42< 0.29755 41 8 R (0.19512195 0.80487805) *
#> 3) V39< 0.1744 38 3 R (0.07894737 0.92105263) *
#>
#> $trees[[8]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 58 M (0.58273381 0.41726619)
#> 2) V28>=0.9202 40 1 M (0.97500000 0.02500000) *
#> 3) V28< 0.9202 99 42 R (0.42424242 0.57575758)
#> 6) V1>=0.02045 65 25 M (0.61538462 0.38461538)
#> 12) V51>=0.01305 37 4 M (0.89189189 0.10810811)
#> 24) V12>=0.1231 30 0 M (1.00000000 0.00000000) *
#> 25) V12< 0.1231 7 3 R (0.42857143 0.57142857) *
#> 13) V51< 0.01305 28 7 R (0.25000000 0.75000000)
#> 26) V36< 0.44355 11 4 M (0.63636364 0.36363636) *
#> 27) V36>=0.44355 17 0 R (0.00000000 1.00000000) *
#> 7) V1< 0.02045 34 2 R (0.05882353 0.94117647) *
#>
#> $trees[[9]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 56 M (0.59712230 0.40287770)
#> 2) V47>=0.06225 111 32 M (0.71171171 0.28828829)
#> 4) V37< 0.48 80 11 M (0.86250000 0.13750000)
#> 8) V15< 0.57285 71 4 M (0.94366197 0.05633803)
#> 16) V9>=0.109 64 0 M (1.00000000 0.00000000) *
#> 17) V9< 0.109 7 3 R (0.42857143 0.57142857) *
#> 9) V15>=0.57285 9 2 R (0.22222222 0.77777778) *
#> 5) V37>=0.48 31 10 R (0.32258065 0.67741935)
#> 10) V27>=0.82755 8 0 M (1.00000000 0.00000000) *
#> 11) V27< 0.82755 23 2 R (0.08695652 0.91304348) *
#> 3) V47< 0.06225 28 4 R (0.14285714 0.85714286)
#> 6) V53< 0.0046 7 3 M (0.57142857 0.42857143) *
#> 7) V53>=0.0046 21 0 R (0.00000000 1.00000000) *
#>
#> $trees[[10]]
#> n= 139
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 139 68 M (0.5107914 0.4892086)
#> 2) V48>=0.0503 99 32 M (0.6767677 0.3232323)
#> 4) V51>=0.01305 60 7 M (0.8833333 0.1166667) *
#> 5) V51< 0.01305 39 14 R (0.3589744 0.6410256)
#> 10) V59>=0.0104 10 0 M (1.0000000 0.0000000) *
#> 11) V59< 0.0104 29 4 R (0.1379310 0.8620690)
#> 22) V14< 0.25965 7 3 M (0.5714286 0.4285714) *
#> 23) V14>=0.25965 22 0 R (0.0000000 1.0000000) *
#> 3) V48< 0.0503 40 4 R (0.1000000 0.9000000) *
#>
#>
#> $weights
#> [1] 0.8090480 0.8476408 0.7270150 0.7069392 0.7366673 0.6694236 0.5799773
#> [8] 0.7974748 0.7966370 0.6146628
#>
#> $votes
#> [,1] [,2]
#> [1,] 1.9015792 5.3839065
#> [2,] 2.1738375 5.1116482
#> [3,] 2.3145392 4.9709466
#> [4,] 2.2314289 5.0540569
#> [5,] 2.1317273 5.1537585
#> [6,] 2.4417526 4.8437332
#> [7,] 2.3431901 4.9422957
#> [8,] 1.3216020 5.9638838
#> [9,] 0.6146628 6.6708230
#> [10,] 2.0582693 5.2272164
#> [11,] 2.1893186 5.0961672
#> [12,] 0.7270150 6.5584708
#> [13,] 2.1391526 5.1463332
#> [14,] 2.8758199 4.4096659
#> [15,] 2.2242551 5.0612307
#> [16,] 2.9000148 4.3854710
#> [17,] 1.3964386 5.8890472
#> [18,] 1.9216551 5.3638307
#> [19,] 1.4121376 5.8733482
#> [20,] 1.1946401 6.0908457
#> [21,] 0.0000000 7.2854858
#> [22,] 0.8476408 6.4378450
#> [23,] 2.6731117 4.6123741
#> [24,] 1.5843082 5.7011776
#> [25,] 1.5843082 5.7011776
#> [26,] 1.4636823 5.8218035
#> [27,] 2.2151389 5.0703468
#> [28,] 1.5035762 5.7819096
#> [29,] 0.7974748 6.4880110
#> [30,] 1.1946401 6.0908457
#> [31,] 1.3964386 5.8890472
#> [32,] 0.0000000 7.2854858
#> [33,] 2.0486170 5.2368688
#> [34,] 1.3069922 5.9784936
#> [35,] 1.3216020 5.9638838
#> [36,] 0.7069392 6.5785466
#> [37,] 2.1893186 5.0961672
#> [38,] 0.0000000 7.2854858
#> [39,] 1.5035762 5.7819096
#> [40,] 2.1190768 5.1664090
#> [41,] 1.3216020 5.9638838
#> [42,] 0.0000000 7.2854858
#> [43,] 0.0000000 7.2854858
#> [44,] 0.7366673 6.5488184
#> [45,] 0.7974748 6.4880110
#> [46,] 0.6146628 6.6708230
#> [47,] 1.3416778 5.9438080
#> [48,] 2.0036881 5.2817977
#> [49,] 1.5941118 5.6913740
#> [50,] 1.3766143 5.9088715
#> [51,] 0.0000000 7.2854858
#> [52,] 1.5545800 5.7309058
#> [53,] 1.5545800 5.7309058
#> [54,] 1.5545800 5.7309058
#> [55,] 1.9921149 5.2933709
#> [56,] 2.9124257 4.3730601
#> [57,] 2.3721306 4.9133552
#> [58,] 2.0111014 5.2743844
#> [59,] 2.1331059 5.1523799
#> [60,] 1.3416778 5.9438080
#> [61,] 0.6694236 6.6160622
#> [62,] 2.9011040 4.3843818
#> [63,] 1.5333044 5.7521814
#> [64,] 2.1479672 5.1375186
#> [65,] 5.7309058 1.5545800
#> [66,] 4.9709466 2.3145392
#> [67,] 4.4652650 2.8202208
#> [68,] 4.9342688 2.3512170
#> [69,] 5.1124860 2.1729998
#> [70,] 4.4854122 2.8000736
#> [71,] 5.2019323 2.0835534
#> [72,] 4.2129024 3.0725834
#> [73,] 6.6708230 0.6146628
#> [74,] 4.9643590 2.3211268
#> [75,] 5.9088715 1.3766143
#> [76,] 5.8194252 1.4660606
#> [77,] 5.2817977 2.0036881
#> [78,] 5.1923514 2.0931344
#> [79,] 5.6403702 1.6451156
#> [80,] 6.6160622 0.6694236
#> [81,] 6.6708230 0.6146628
#> [82,] 4.5000220 2.7854638
#> [83,] 5.1463332 2.1391526
#> [84,] 6.0360849 1.2494009
#> [85,] 5.2817977 2.0036881
#> [86,] 6.4764378 0.8090480
#> [87,] 5.7521814 1.5333044
#> [88,] 5.6412079 1.6442779
#> [89,] 4.9844348 2.3010510
#> [90,] 5.7819096 1.5035762
#> [91,] 6.4888488 0.7966370
#> [92,] 4.9342688 2.3512170
#> [93,] 4.9431334 2.3423524
#> [94,] 6.4378450 0.8476408
#> [95,] 4.9717843 2.3137015
#> [96,] 5.8218035 1.4636823
#> [97,] 5.7011776 1.5843082
#> [98,] 6.4378450 0.8476408
#> [99,] 5.8890472 1.3964386
#> [100,] 4.5547827 2.7307030
#> [101,] 7.2854858 0.0000000
#> [102,] 5.8185874 1.4668984
#> [103,] 4.3719708 2.9135150
#> [104,] 6.5488184 0.7366673
#> [105,] 5.7513437 1.5341421
#> [106,] 6.5488184 0.7366673
#> [107,] 6.5488184 0.7366673
#> [108,] 6.4378450 0.8476408
#> [109,] 4.3941854 2.8913004
#> [110,] 5.8617750 1.4237108
#> [111,] 5.7810718 1.5044139
#> [112,] 5.7694986 1.5159871
#> [113,] 5.8185874 1.4668984
#> [114,] 5.2817977 2.0036881
#> [115,] 5.2817977 2.0036881
#> [116,] 3.7773838 3.5081020
#> [117,] 6.7055085 0.5799773
#> [118,] 6.5488184 0.7366673
#> [119,] 6.5488184 0.7366673
#> [120,] 7.2854858 0.0000000
#> [121,] 6.5488184 0.7366673
#> [122,] 5.0865148 2.1989710
#> [123,] 5.9688412 1.3166446
#> [124,] 5.2994176 1.9860682
#> [125,] 5.9688412 1.3166446
#> [126,] 5.0703468 2.2151389
#> [127,] 5.9688412 1.3166446
#> [128,] 5.9688412 1.3166446
#> [129,] 6.7055085 0.5799773
#> [130,] 6.7055085 0.5799773
#> [131,] 6.7055085 0.5799773
#> [132,] 6.7055085 0.5799773
#> [133,] 5.8964605 1.3890252
#> [134,] 6.7055085 0.5799773
#> [135,] 6.7055085 0.5799773
#> [136,] 6.5488184 0.7366673
#> [137,] 5.9688412 1.3166446
#> [138,] 5.9688412 1.3166446
#> [139,] 5.2817977 2.0036881
#>
#> $prob
#> [,1] [,2]
#> [1,] 0.26100926 0.73899074
#> [2,] 0.29837922 0.70162078
#> [3,] 0.31769182 0.68230818
#> [4,] 0.30628416 0.69371584
#> [5,] 0.29259919 0.70740081
#> [6,] 0.33515303 0.66484697
#> [7,] 0.32162442 0.67837558
#> [8,] 0.18140204 0.81859796
#> [9,] 0.08436813 0.91563187
#> [10,] 0.28251642 0.71748358
#> [11,] 0.30050414 0.69949586
#> [12,] 0.09978950 0.90021050
#> [13,] 0.29361839 0.70638161
#> [14,] 0.39473276 0.60526724
#> [15,] 0.30529949 0.69470051
#> [16,] 0.39805373 0.60194627
#> [17,] 0.19167405 0.80832595
#> [18,] 0.26376485 0.73623515
#> [19,] 0.19382889 0.80617111
#> [20,] 0.16397535 0.83602465
#> [21,] 0.00000000 1.00000000
#> [22,] 0.11634651 0.88365349
#> [23,] 0.36690919 0.63309081
#> [24,] 0.21746088 0.78253912
#> [25,] 0.21746088 0.78253912
#> [26,] 0.20090387 0.79909613
#> [27,] 0.30404821 0.69595179
#> [28,] 0.20637968 0.79362032
#> [29,] 0.10946076 0.89053924
#> [30,] 0.16397535 0.83602465
#> [31,] 0.19167405 0.80832595
#> [32,] 0.00000000 1.00000000
#> [33,] 0.28119154 0.71880846
#> [34,] 0.17939672 0.82060328
#> [35,] 0.18140204 0.81859796
#> [36,] 0.09703391 0.90296609
#> [37,] 0.30050414 0.69949586
#> [38,] 0.00000000 1.00000000
#> [39,] 0.20637968 0.79362032
#> [40,] 0.29086280 0.70913720
#> [41,] 0.18140204 0.81859796
#> [42,] 0.00000000 1.00000000
#> [43,] 0.00000000 1.00000000
#> [44,] 0.10111438 0.89888562
#> [45,] 0.10946076 0.89053924
#> [46,] 0.08436813 0.91563187
#> [47,] 0.18415763 0.81584237
#> [48,] 0.27502463 0.72497537
#> [49,] 0.21880652 0.78119348
#> [50,] 0.18895298 0.81104702
#> [51,] 0.00000000 1.00000000
#> [52,] 0.21338042 0.78661958
#> [53,] 0.21338042 0.78661958
#> [54,] 0.21338042 0.78661958
#> [55,] 0.27343611 0.72656389
#> [56,] 0.39975724 0.60024276
#> [57,] 0.32559676 0.67440324
#> [58,] 0.27604218 0.72395782
#> [59,] 0.29278843 0.70721157
#> [60,] 0.18415763 0.81584237
#> [61,] 0.09188455 0.90811545
#> [62,] 0.39820324 0.60179676
#> [63,] 0.21046014 0.78953986
#> [64,] 0.29482827 0.70517173
#> [65,] 0.78661958 0.21338042
#> [66,] 0.68230818 0.31769182
#> [67,] 0.61289873 0.38710127
#> [68,] 0.67727382 0.32272618
#> [69,] 0.70173577 0.29826423
#> [70,] 0.61566412 0.38433588
#> [71,] 0.71401311 0.28598689
#> [72,] 0.57825964 0.42174036
#> [73,] 0.91563187 0.08436813
#> [74,] 0.68140398 0.31859602
#> [75,] 0.81104702 0.18895298
#> [76,] 0.79876968 0.20123032
#> [77,] 0.72497537 0.27502463
#> [78,] 0.71269803 0.28730197
#> [79,] 0.77419274 0.22580726
#> [80,] 0.90811545 0.09188455
#> [81,] 0.91563187 0.08436813
#> [82,] 0.61766945 0.38233055
#> [83,] 0.70638161 0.29361839
#> [84,] 0.82850823 0.17149177
#> [85,] 0.72497537 0.27502463
#> [86,] 0.88895071 0.11104929
#> [87,] 0.78953986 0.21046014
#> [88,] 0.77430773 0.22569227
#> [89,] 0.68415957 0.31584043
#> [90,] 0.79362032 0.20637968
#> [91,] 0.89065423 0.10934577
#> [92,] 0.67727382 0.32272618
#> [93,] 0.67849057 0.32150943
#> [94,] 0.88365349 0.11634651
#> [95,] 0.68242317 0.31757683
#> [96,] 0.79909613 0.20090387
#> [97,] 0.78253912 0.21746088
#> [98,] 0.88365349 0.11634651
#> [99,] 0.80832595 0.19167405
#> [100,] 0.62518587 0.37481413
#> [101,] 1.00000000 0.00000000
#> [102,] 0.79865469 0.20134531
#> [103,] 0.60009324 0.39990676
#> [104,] 0.89888562 0.10111438
#> [105,] 0.78942487 0.21057513
#> [106,] 0.89888562 0.10111438
#> [107,] 0.89888562 0.10111438
#> [108,] 0.88365349 0.11634651
#> [109,] 0.60314240 0.39685760
#> [110,] 0.80458258 0.19541742
#> [111,] 0.79350534 0.20649466
#> [112,] 0.79191681 0.20808319
#> [113,] 0.79865469 0.20134531
#> [114,] 0.72497537 0.27502463
#> [115,] 0.72497537 0.27502463
#> [116,] 0.51848070 0.48151930
#> [117,] 0.92039278 0.07960722
#> [118,] 0.89888562 0.10111438
#> [119,] 0.89888562 0.10111438
#> [120,] 1.00000000 0.00000000
#> [121,] 0.89888562 0.10111438
#> [122,] 0.69817098 0.30182902
#> [123,] 0.81927841 0.18072159
#> [124,] 0.72739385 0.27260615
#> [125,] 0.81927841 0.18072159
#> [126,] 0.69595179 0.30404821
#> [127,] 0.81927841 0.18072159
#> [128,] 0.81927841 0.18072159
#> [129,] 0.92039278 0.07960722
#> [130,] 0.92039278 0.07960722
#> [131,] 0.92039278 0.07960722
#> [132,] 0.92039278 0.07960722
#> [133,] 0.80934350 0.19065650
#> [134,] 0.92039278 0.07960722
#> [135,] 0.92039278 0.07960722
#> [136,] 0.89888562 0.10111438
#> [137,] 0.81927841 0.18072159
#> [138,] 0.81927841 0.18072159
#> [139,] 0.72497537 0.27502463
#>
#> $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" "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 V16
#> 5.3776257 0.5242910 7.1117895 3.1682893 0.0000000 0.5649714 3.2617455 2.8866699
#> V17 V18 V19 V2 V20 V21 V22 V23
#> 0.0000000 0.0000000 0.0000000 0.0000000 1.9513202 2.6085774 0.0000000 2.6777474
#> V24 V25 V26 V27 V28 V29 V3 V30
#> 0.0000000 0.0000000 0.0000000 4.5631442 3.6530966 0.0000000 0.0000000 0.0000000
#> V31 V32 V33 V34 V35 V36 V37 V38
#> 1.2287225 3.5883069 1.8358950 0.0000000 3.7334511 1.1432839 4.5274329 0.0000000
#> V39 V4 V40 V41 V42 V43 V44 V45
#> 2.3398291 0.0000000 0.0000000 0.0000000 1.9537011 0.0000000 3.3746110 0.0000000
#> V46 V47 V48 V49 V5 V50 V51 V52
#> 0.0000000 3.0554280 3.0878914 5.8265979 0.0000000 1.5940027 7.1276470 4.6528495
#> V53 V54 V55 V56 V57 V58 V59 V6
#> 0.7239133 0.0000000 0.0000000 2.7146123 0.0000000 0.0000000 3.2878842 0.0000000
#> V60 V7 V8 V9
#> 1.2355298 0.0000000 0.0000000 4.6191427
#>
#> $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"
#>
#> $call
#> adabag::boosting(formula = formula, data = data, mfinal = 10L,
#> control = list(minsplit = 20L, minbucket = 7, cp = 0.01,
#> maxcompete = 4L, maxsurrogate = 5L, usesurrogate = 2L,
#> surrogatestyle = 0L, maxdepth = 30L, xval = 0L))
#>
#> attr(,"vardep.summary")
#> M R
#> 75 64
#> attr(,"class")
#> [1] "boosting"
print(learner$importance())
#> V51 V11 V49 V1 V52 V9 V27 V37
#> 7.1276470 7.1117895 5.8265979 5.3776257 4.6528495 4.6191427 4.5631442 4.5274329
#> V35 V28 V32 V44 V59 V15 V12 V48
#> 3.7334511 3.6530966 3.5883069 3.3746110 3.2878842 3.2617455 3.1682893 3.0878914
#> V47 V16 V56 V23 V21 V39 V42 V20
#> 3.0554280 2.8866699 2.7146123 2.6777474 2.6085774 2.3398291 1.9537011 1.9513202
#> V33 V50 V60 V31 V36 V53 V14 V10
#> 1.8358950 1.5940027 1.2355298 1.2287225 1.1432839 0.7239133 0.5649714 0.5242910
#> V13 V17 V18 V19 V2 V22 V24 V25
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V26 V29 V3 V30 V34 V38 V4 V40
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V41 V43 V45 V46 V5 V54 V55 V57
#> 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> V58 V6 V7 V8
#> 0.0000000 0.0000000 0.0000000 0.0000000
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.2028986