wrap_lightgbm.RdFits a `lightgbm` model with a consistent interface. Supports binary classification, multiclass classification, and regression.
A matrix or data.frame of features.
A factor or character vector for classification, numeric for regression.
Additional arguments passed to [lightgbm::lgb.train()]. Pass `params = list(objective = "binary")` for binary classification, `params = list(objective = "multiclass", num_class = k)` for multiclass, or `params = list(objective = "regression")` for regression.
A fitted `wrap_lightgbm` object.
A matrix or data.frame of new observations.
`"class"` (default) for class labels, `"prob"` for a probability matrix. Ignored for regression.
An object of class `wrap_lightgbm` with fields:
The fitted lgb.Booster model.
Class levels (NULL for regression).
"classification" or "regression".
The lightgbm objective string, stored at fit time.
# \donttest{
X <- as.matrix(iris[, 1:4])
y <- iris$Species
mod <- wrap_lightgbm(X, y,
params = list(objective = "multiclass", num_class = 3, verbose = -1),
nrounds = 50)
predict(mod, newx = X, type = "class")
#> [1] setosa versicolor setosa virginica virginica virginica
#> [7] versicolor versicolor virginica setosa virginica virginica
#> [13] versicolor versicolor setosa versicolor versicolor versicolor
#> [19] virginica setosa virginica setosa versicolor versicolor
#> [25] versicolor virginica versicolor virginica versicolor setosa
#> [31] versicolor setosa virginica virginica versicolor versicolor
#> [37] virginica virginica virginica virginica versicolor virginica
#> [43] setosa setosa versicolor virginica setosa setosa
#> [49] virginica virginica versicolor setosa virginica setosa
#> [55] virginica setosa setosa virginica setosa versicolor
#> [61] setosa setosa virginica virginica virginica setosa
#> [67] virginica virginica versicolor versicolor versicolor virginica
#> [73] versicolor setosa virginica setosa virginica versicolor
#> [79] virginica versicolor virginica versicolor setosa setosa
#> [85] versicolor versicolor virginica virginica virginica virginica
#> [91] versicolor setosa setosa setosa versicolor virginica
#> [97] setosa setosa virginica virginica versicolor setosa
#> [103] virginica setosa setosa virginica versicolor virginica
#> [109] versicolor virginica setosa versicolor virginica virginica
#> [115] virginica setosa virginica versicolor setosa setosa
#> [121] virginica setosa virginica versicolor setosa virginica
#> [127] setosa virginica versicolor setosa versicolor setosa
#> [133] virginica versicolor virginica setosa versicolor setosa
#> [139] versicolor versicolor virginica versicolor virginica versicolor
#> [145] setosa versicolor virginica virginica setosa versicolor
#> Levels: setosa versicolor virginica
predict(mod, newx = X, type = "prob")
#> setosa versicolor virginica
#> [1,] 0.9986147567 0.9962126949 0.9984018480
#> [2,] 0.9977728357 0.9989016863 0.9984931982
#> [3,] 0.9989828918 0.9989419659 0.9959894302
#> [4,] 0.9977728357 0.9985523684 0.9990382600
#> [5,] 0.9962126949 0.9962126949 0.9977247002
#> [6,] 0.9980721446 0.9984671374 0.9986005889
#> [7,] 0.9980027753 0.9986652607 0.9984946015
#> [8,] 0.9986669057 0.9990007686 0.9774081502
#> [9,] 0.9857874548 0.9960895076 0.9989368491
#> [10,] 0.9986184500 0.9985386102 0.9984763164
#> [11,] 0.9977827497 0.9985275020 0.9986184500
#> [12,] 0.9979871066 0.9977728357 0.9981974451
#> [13,] 0.9979871066 0.9990007686 0.9962126949
#> [14,] 0.9986682924 0.9988898684 0.9926205907
#> [15,] 0.9984018480 0.9865292199 0.9758224444
#> [16,] 0.9961656333 0.9986857239 0.9984018480
#> [17,] 0.9985523684 0.9987268086 0.0018178409
#> [18,] 0.0018256953 0.0019224938 0.0007416751
#> [19,] 0.0013648784 0.0009540648 0.0028850052
#> [20,] 0.0094846168 0.0009500176 0.0010193919
#> [21,] 0.0083119645 0.0008215997 0.0089882665
#> [22,] 0.0032304219 0.0007625947 0.0008305756
#> [23,] 0.0012045085 0.0033157356 0.0032712631
#> [24,] 0.0008923537 0.0141417046 0.0024476969
#> [25,] 0.0020386177 0.0037222123 0.0007648521
#> [26,] 0.0008305154 0.0013998754 0.0065825097
#> [27,] 0.0031368054 0.0037809784 0.0009337228
#> [28,] 0.0040187320 0.0008176463 0.0117758340
#> [29,] 0.0017006188 0.0058767718 0.0013037954
#> [30,] 0.0008864234 0.0007748374 0.0006569446
#> [31,] 0.0008656160 0.0026112892 0.0007522154
#> [32,] 0.0083233832 0.0007006698 0.0008906564
#> [33,] 0.0006572452 0.0020096064 0.0424368756
#> [34,] 0.0007940808 0.0009054198 0.0012031154
#> [35,] 0.0005633298 0.0011856105 0.0005407432
#> [36,] 0.0005633298 0.0059203757 0.0017623166
#> [37,] 0.0012589065 0.0009432480 0.0043761918
#> [38,] 0.0005521025 0.0005628444 0.0012346528
#> [39,] 0.0010918079 0.0010282698 0.0015505440
#> [40,] 0.0009432480 0.0004420997 0.0035112897
#> [41,] 0.0009432015 0.0012712242 0.0004525266
#> [42,] 0.0028122031 0.0009432480 0.0058489714
#> [43,] 0.0041371095 0.0031131244 0.0004405147
#> [44,] 0.0040028253 0.0004933308 0.0009548471
#> [45,] 0.0004405147 0.0051369287 0.0020424741
#> [46,] 0.0005633298 0.0009054198 0.0023751359
#> [47,] 0.0041137760 0.0005628444 0.0005633298
#> [48,] 0.0038792474 0.0012031154 0.0009432015
#> [49,] 0.0009432480 0.0006390530 0.0013160138
#> [50,] 0.0006217163 0.0009051072 0.0044405697
#> [51,] 0.0009945952 0.0017273653 0.0009976088
#> [52,] 0.0013411531 0.0007202752 0.0011163092
#> [53,] 0.0005683628 0.0006859434 0.0019499027
#> [54,] 0.0013411531 0.0010630621 0.0005271885
#> [55,] 0.0017273653 0.0017273653 0.0018849999
#> [56,] 0.0015373915 0.0011360948 0.0010023773
#> [57,] 0.0016067143 0.0009438336 0.0011210922
#> [58,] 0.0009423978 0.0005575787 0.0215907664
#> [59,] 0.0128715649 0.0020858020 0.0006853089
#> [60,] 0.0009969550 0.0010708804 0.0009326097
#> [61,] 0.0013315388 0.0010819649 0.0009969550
#> [62,] 0.0016224908 0.0013411531 0.0012885662
#> [63,] 0.0016224908 0.0005575787 0.0017273653
#> [64,] 0.0009472006 0.0007259126 0.0038047124
#> [65,] 0.0009976088 0.0125761160 0.0229764695
#> [66,] 0.0017408233 0.0009297891 0.0009976088
#> [67,] 0.0010630621 0.0008602106 0.9963743958
#> [68,] 0.9945598335 0.9833956543 0.9958611084
#> [69,] 0.9911833428 0.9964711858 0.9916904219
#> [70,] 0.9787435762 0.9976516203 0.9969197090
#> [71,] 0.9810633542 0.9964775572 0.9790262896
#> [72,] 0.9763241919 0.9972933913 0.9981638410
#> [73,] 0.9944922154 0.9950810775 0.9710464737
#> [74,] 0.9948737554 0.7854602155 0.9875145510
#> [75,] 0.9286942018 0.9766002156 0.9970682722
#> [76,] 0.9980916115 0.9857668583 0.6352710896
#> [77,] 0.9800901049 0.9920512365 0.9946338510
#> [78,] 0.9915638817 0.9975688346 0.6269965434
#> [79,] 0.9922236816 0.9871581961 0.9960952096
#> [80,] 0.9948938396 0.9972803080 0.9963339441
#> [81,] 0.9955211274 0.9834024794 0.9958907898
#> [82,] 0.9824111111 0.9979648252 0.9968835435
#> [83,] 0.9977317468 0.9902294550 0.9493576658
#> [84,] 0.9971817365 0.0015644449 0.0069218182
#> [85,] 0.0032273172 0.0032963589 0.0015892837
#> [86,] 0.0032273172 0.0846757675 0.0095375910
#> [87,] 0.0072782243 0.0031769089 0.0181522165
#> [88,] 0.0022337788 0.0040861333 0.0134578466
#> [89,] 0.0055568894 0.0027343649 0.0067885757
#> [90,] 0.0031769089 0.0020989185 0.1995323308
#> [91,] 0.0032260906 0.0182177640 0.0020833389
#> [92,] 0.1185094617 0.0031769089 0.0235055594
#> [93,] 0.0772078577 0.0521380316 0.0010404174
#> [94,] 0.0877083683 0.0025158814 0.0032159751
#> [95,] 0.0010404174 0.1723860312 0.0460444980
#> [96,] 0.0032273172 0.0015644449 0.0103987954
#> [97,] 0.1182862768 0.0040861333 0.0032273172
#> [98,] 0.0526047109 0.0069218182 0.0032260906
#> [99,] 0.0031769089 0.0055483228 0.0384449043
#> [100,] 0.0027691629 0.0019091219 0.0352045217
#> [101,] 0.0003906481 0.0020599398 0.0006005432
#> [102,] 0.0008860112 0.0003780385 0.0003904926
#> [103,] 0.0004487454 0.0003720907 0.0020606672
#> [104,] 0.0008860112 0.0003845696 0.0004345515
#> [105,] 0.0020599398 0.0020599398 0.0003902999
#> [106,] 0.0003904639 0.0003967677 0.0003970338
#> [107,] 0.0003905103 0.0003909057 0.0003843063
#> [108,] 0.0003906966 0.0004416527 0.0010010834
#> [109,] 0.0013409803 0.0018246904 0.0003778420
#> [110,] 0.0003845950 0.0003905094 0.0005910739
#> [111,] 0.0008857115 0.0003905331 0.0003845950
#> [112,] 0.0003904026 0.0008860112 0.0005139887
#> [113,] 0.0003904026 0.0004416527 0.0020599398
#> [114,] 0.0003845070 0.0003842190 0.0035746969
#> [115,] 0.0006005432 0.0008946641 0.0012010860
#> [116,] 0.0020935434 0.0003844870 0.0006005432
#> [117,] 0.0003845696 0.0004129808 0.0018077634
#> [118,] 0.0036144712 0.0146818519 0.0033972165
#> [119,] 0.0074517788 0.0025747493 0.0054245729
#> [120,] 0.0117718070 0.0013983621 0.0020608991
#> [121,] 0.0106246813 0.0027008431 0.0119854439
#> [122,] 0.0204453863 0.0019440140 0.0010055834
#> [123,] 0.0043032761 0.0016031869 0.0256822632
#> [124,] 0.0042338908 0.2003980798 0.0100377521
#> [125,] 0.0692671805 0.0196775721 0.0021668757
#> [126,] 0.0010778731 0.0128332663 0.3581464007
#> [127,] 0.0167730897 0.0041677851 0.0044324262
#> [128,] 0.0044173863 0.0016135190 0.3612276226
#> [129,] 0.0060756997 0.0069650321 0.0026009950
#> [130,] 0.0042197370 0.0019448546 0.0030091114
#> [131,] 0.0036132565 0.0139862314 0.0033569948
#> [132,] 0.0092655057 0.0013345050 0.0022258001
#> [133,] 0.0016110080 0.0077609386 0.0082054585
#> [134,] 0.0020241827 0.9975301353 0.9918750664
#> [135,] 0.9962093530 0.9955180306 0.9978699731
#> [136,] 0.9962093530 0.9094038568 0.9887000924
#> [137,] 0.9914628692 0.9958798432 0.9774715917
#> [138,] 0.9972141187 0.9953510223 0.9853075007
#> [139,] 0.9933513028 0.9962373654 0.9916608804
#> [140,] 0.9958798432 0.9974589818 0.7969563795
#> [141,] 0.9958307080 0.9805110117 0.9974641345
#> [142,] 0.8786783352 0.9958798432 0.9706454692
#> [143,] 0.9186550328 0.9447488440 0.9985190679
#> [144,] 0.9082888064 0.9969907877 0.9958291779
#> [145,] 0.9985190679 0.8224770401 0.9519130279
#> [146,] 0.9962093530 0.9975301353 0.9872260687
#> [147,] 0.8775999472 0.9953510223 0.9962093530
#> [148,] 0.9435160417 0.9918750664 0.9958307080
#> [149,] 0.9958798432 0.9938126242 0.9602390818
#> [150,] 0.9966091208 0.9971857708 0.9603549086
# }