wrap_svm.RdFits an `e1071` support vector machine with a consistent interface. Supports classification and regression.
A matrix or data.frame of features.
A factor or character vector for classification, numeric for regression.
Additional arguments passed to [e1071::svm()]. `probability = TRUE` is set automatically for classification; do not override this if you need `type = "prob"` predictions.
A fitted `wrap_svm` 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_svm` with fields:
The fitted svm model.
Class levels (NULL for regression).
"classification" or "regression".
# \donttest{
X <- as.matrix(iris[, 1:4])
y <- iris$Species
mod <- wrap_svm(X, y, kernel = "radial")
predict(mod, newx = X, type = "class")
#> 1 2 3 4 5 6 7
#> setosa setosa setosa setosa setosa setosa setosa
#> 8 9 10 11 12 13 14
#> setosa setosa setosa setosa setosa setosa setosa
#> 15 16 17 18 19 20 21
#> setosa setosa setosa setosa setosa setosa setosa
#> 22 23 24 25 26 27 28
#> setosa setosa setosa setosa setosa setosa setosa
#> 29 30 31 32 33 34 35
#> setosa setosa setosa setosa setosa setosa setosa
#> 36 37 38 39 40 41 42
#> setosa setosa setosa setosa setosa setosa setosa
#> 43 44 45 46 47 48 49
#> setosa setosa setosa setosa setosa setosa setosa
#> 50 51 52 53 54 55 56
#> setosa versicolor versicolor versicolor versicolor versicolor versicolor
#> 57 58 59 60 61 62 63
#> versicolor versicolor versicolor versicolor versicolor versicolor versicolor
#> 64 65 66 67 68 69 70
#> versicolor versicolor versicolor versicolor versicolor versicolor versicolor
#> 71 72 73 74 75 76 77
#> versicolor versicolor versicolor versicolor versicolor versicolor versicolor
#> 78 79 80 81 82 83 84
#> virginica versicolor versicolor versicolor versicolor versicolor virginica
#> 85 86 87 88 89 90 91
#> versicolor versicolor versicolor versicolor versicolor versicolor versicolor
#> 92 93 94 95 96 97 98
#> versicolor versicolor versicolor versicolor versicolor versicolor versicolor
#> 99 100 101 102 103 104 105
#> versicolor versicolor virginica virginica virginica virginica virginica
#> 106 107 108 109 110 111 112
#> virginica virginica virginica virginica virginica virginica virginica
#> 113 114 115 116 117 118 119
#> virginica virginica virginica virginica virginica virginica virginica
#> 120 121 122 123 124 125 126
#> versicolor virginica virginica virginica virginica virginica virginica
#> 127 128 129 130 131 132 133
#> virginica virginica virginica virginica virginica virginica virginica
#> 134 135 136 137 138 139 140
#> versicolor virginica virginica virginica virginica virginica virginica
#> 141 142 143 144 145 146 147
#> virginica virginica virginica virginica virginica virginica virginica
#> 148 149 150
#> virginica virginica virginica
#> Levels: setosa versicolor virginica
predict(mod, newx = X, type = "prob")
#> setosa versicolor virginica
#> 1 0.979810766 0.011071123 0.009118111
#> 2 0.972492127 0.017737870 0.009770002
#> 3 0.978472469 0.011683562 0.009843969
#> 4 0.974434676 0.015000888 0.010564436
#> 5 0.978965358 0.011378219 0.009656422
#> 6 0.973597994 0.016387093 0.010014913
#> 7 0.975082167 0.013217553 0.011700280
#> 8 0.979686429 0.011211192 0.009102379
#> 9 0.965639577 0.022022558 0.012337864
#> 10 0.976263334 0.013818981 0.009917685
#> 11 0.976001562 0.014024718 0.009973720
#> 12 0.978159119 0.011872120 0.009968761
#> 13 0.974802168 0.015242481 0.009955351
#> 14 0.966683531 0.018416709 0.014899760
#> 15 0.964196626 0.020883482 0.014919892
#> 16 0.958052789 0.022867950 0.019079261
#> 17 0.975293104 0.014576530 0.010130366
#> 18 0.979497314 0.011659446 0.008843240
#> 19 0.966370756 0.021465128 0.012164116
#> 20 0.976571772 0.013419876 0.010008352
#> 21 0.970820961 0.017687835 0.011491203
#> 22 0.977099639 0.013561291 0.009339070
#> 23 0.967184458 0.017069940 0.015745602
#> 24 0.966406020 0.022410023 0.011183957
#> 25 0.974943280 0.014220281 0.010836439
#> 26 0.966896372 0.022385110 0.010718517
#> 27 0.976356343 0.014314271 0.009329386
#> 28 0.978646780 0.012077409 0.009275811
#> 29 0.978540939 0.012003856 0.009455205
#> 30 0.976267305 0.013571176 0.010161519
#> 31 0.974502697 0.015689566 0.009807737
#> 32 0.969189320 0.019543959 0.011266721
#> 33 0.965772991 0.019140914 0.015086095
#> 34 0.967449539 0.018314700 0.014235761
#> 35 0.975336120 0.015102158 0.009561722
#> 36 0.979083560 0.011584032 0.009332408
#> 37 0.972696265 0.015658574 0.011645161
#> 38 0.977039429 0.011824118 0.011136453
#> 39 0.970073118 0.017499355 0.012427527
#> 40 0.979171525 0.011669429 0.009159046
#> 41 0.979912102 0.011070664 0.009017234
#> 42 0.863320457 0.115722964 0.020956579
#> 43 0.971585143 0.014734937 0.013679920
#> 44 0.970124858 0.018926158 0.010948984
#> 45 0.972566446 0.016892180 0.010541375
#> 46 0.970529042 0.019681043 0.009789916
#> 47 0.975867408 0.013600269 0.010532323
#> 48 0.976520113 0.012781810 0.010698078
#> 49 0.977313711 0.013066546 0.009619743
#> 50 0.979602907 0.011297024 0.009100069
#> 51 0.018016668 0.942018350 0.039964982
#> 52 0.010682753 0.965117572 0.024199675
#> 53 0.014830190 0.859804248 0.125365561
#> 54 0.005799533 0.957146161 0.037054306
#> 55 0.009779906 0.907895227 0.082324867
#> 56 0.007149602 0.971260708 0.021589690
#> 57 0.011829717 0.912014932 0.076155350
#> 58 0.027886628 0.951713016 0.020400355
#> 59 0.011822726 0.975503052 0.012674222
#> 60 0.008993852 0.953833029 0.037173119
#> 61 0.026659816 0.934894488 0.038445696
#> 62 0.008859894 0.971546277 0.019593829
#> 63 0.014622813 0.976182030 0.009195156
#> 64 0.007454222 0.950431500 0.042114278
#> 65 0.016311091 0.978699217 0.004989693
#> 66 0.012624299 0.975527774 0.011847927
#> 67 0.010165847 0.934170603 0.055663550
#> 68 0.011129594 0.986002734 0.002867672
#> 69 0.017303024 0.821846757 0.160850219
#> 70 0.007052890 0.986876378 0.006070732
#> 71 0.013845393 0.564088177 0.422066430
#> 72 0.008431317 0.987388878 0.004179804
#> 73 0.011747653 0.663743989 0.324508357
#> 74 0.008573569 0.979523401 0.011903031
#> 75 0.009987852 0.984538906 0.005473243
#> 76 0.011023596 0.976628711 0.012347693
#> 77 0.015856842 0.919071960 0.065071198
#> 78 0.011546429 0.421011614 0.567441957
#> 79 0.006989223 0.934307978 0.058702799
#> 80 0.014116449 0.982400433 0.003483117
#> 81 0.007054108 0.984295952 0.008649940
#> 82 0.009208350 0.984754424 0.006037226
#> 83 0.007992899 0.988195602 0.003811499
#> 84 0.008094078 0.299345152 0.692560770
#> 85 0.012912237 0.920042410 0.067045352
#> 86 0.019647043 0.942347912 0.038005045
#> 87 0.011272090 0.929062890 0.059665021
#> 88 0.015499798 0.947165883 0.037334319
#> 89 0.015182742 0.978173470 0.006643788
#> 90 0.005274954 0.970845762 0.023879284
#> 91 0.006177915 0.971363297 0.022458788
#> 92 0.008250729 0.969347722 0.022401549
#> 93 0.006666973 0.987378031 0.005954996
#> 94 0.021610353 0.959550786 0.018838861
#> 95 0.006343639 0.976831446 0.016824915
#> 96 0.015634300 0.979730252 0.004635448
#> 97 0.009582457 0.982715901 0.007701642
#> 98 0.008765624 0.985543059 0.005691318
#> 99 0.026179005 0.960890401 0.012930594
#> 100 0.007785926 0.983905221 0.008308853
#> 101 0.012089155 0.003442997 0.984467848
#> 102 0.006298282 0.026184044 0.967517674
#> 103 0.007069375 0.005040845 0.987889780
#> 104 0.007484530 0.023897441 0.968618029
#> 105 0.006914575 0.001708572 0.991376853
#> 106 0.010105922 0.007615037 0.982279041
#> 107 0.011129194 0.374526737 0.614344069
#> 108 0.009717367 0.016382127 0.973900506
#> 109 0.009837036 0.027094954 0.963068010
#> 110 0.012816247 0.011764075 0.975419677
#> 111 0.010169151 0.070909755 0.918921094
#> 112 0.006943378 0.019370156 0.973686466
#> 113 0.007035070 0.006918701 0.986046229
#> 114 0.006208558 0.018374362 0.975417080
#> 115 0.009040381 0.002081267 0.988878352
#> 116 0.009018679 0.007084258 0.983897063
#> 117 0.008390174 0.047624171 0.943985655
#> 118 0.019440821 0.019584392 0.960974787
#> 119 0.020942381 0.019603845 0.959453774
#> 120 0.014654375 0.581496355 0.403849270
#> 121 0.007401150 0.004004820 0.988594030
#> 122 0.007536338 0.029840806 0.962622856
#> 123 0.013968198 0.016997252 0.969034549
#> 124 0.008303268 0.130802608 0.860894123
#> 125 0.007929268 0.012497656 0.979573076
#> 126 0.008541791 0.028070519 0.963387690
#> 127 0.008492080 0.191068451 0.800439469
#> 128 0.009814490 0.255618646 0.734566863
#> 129 0.006588320 0.002384202 0.991027477
#> 130 0.014223136 0.129980477 0.855796388
#> 131 0.010360472 0.020082950 0.969556577
#> 132 0.021219387 0.027097336 0.951683278
#> 133 0.006863242 0.001581885 0.991554873
#> 134 0.009774786 0.632063898 0.358161317
#> 135 0.011145372 0.336331486 0.652523142
#> 136 0.011459172 0.010652948 0.977887880
#> 137 0.012506786 0.011143401 0.976349813
#> 138 0.009706627 0.068448892 0.921844482
#> 139 0.010151582 0.323133322 0.666715096
#> 140 0.007447350 0.013113483 0.979439166
#> 141 0.007942345 0.002236817 0.989820838
#> 142 0.008867815 0.010368420 0.980763765
#> 143 0.006298282 0.026184044 0.967517674
#> 144 0.007332162 0.002726348 0.989941491
#> 145 0.009680222 0.003893798 0.986425980
#> 146 0.008094219 0.004626548 0.987279233
#> 147 0.008151038 0.048894806 0.942954156
#> 148 0.007649474 0.023158014 0.969192512
#> 149 0.013181916 0.024330207 0.962487876
#> 150 0.009914797 0.156401275 0.833683928
# }