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".
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 0.979980380 0.011614805 0.008404815
#> 2 0.972391168 0.018500879 0.009107953
#> 3 0.978688011 0.012260390 0.009051599
#> 4 0.974570542 0.015703434 0.009726024
#> 5 0.979211727 0.011987551 0.008800722
#> 6 0.973684011 0.017240705 0.009075284
#> 7 0.975426609 0.013894802 0.010678588
#> 8 0.979821033 0.011743067 0.008435900
#> 9 0.965717198 0.023074781 0.011208021
#> 10 0.976337165 0.014441867 0.009220968
#> 11 0.976152113 0.014718194 0.009129693
#> 12 0.978368570 0.012449160 0.009182270
#> 13 0.974856456 0.015937663 0.009205881
#> 14 0.968987539 0.017573093 0.013439368
#> 15 0.966610001 0.020077950 0.013312049
#> 16 0.959098322 0.023914733 0.016986946
#> 17 0.975454203 0.015420766 0.009125031
#> 18 0.979599954 0.012214047 0.008185999
#> 19 0.966411051 0.022415717 0.011173232
#> 20 0.976773840 0.014184638 0.009041522
#> 21 0.970766128 0.018417141 0.010816731
#> 22 0.977217450 0.014239488 0.008543062
#> 23 0.970191399 0.015730991 0.014077610
#> 24 0.966121960 0.023300781 0.010577259
#> 25 0.975095689 0.014866476 0.010037835
#> 26 0.966645564 0.023290265 0.010064170
#> 27 0.976327719 0.014943752 0.008728529
#> 28 0.978759154 0.012643452 0.008597395
#> 29 0.978642404 0.012553507 0.008804089
#> 30 0.976400264 0.014199546 0.009400190
#> 31 0.974486492 0.016379628 0.009133879
#> 32 0.969027931 0.020336914 0.010635155
#> 33 0.966750923 0.019859431 0.013389646
#> 34 0.968310492 0.019051399 0.012638109
#> 35 0.975309055 0.015765348 0.008925597
#> 36 0.979205795 0.012122893 0.008671312
#> 37 0.972827492 0.016341997 0.010830511
#> 38 0.977413376 0.012506010 0.010080613
#> 39 0.970324044 0.018386130 0.011289826
#> 40 0.979271137 0.012209863 0.008519000
#> 41 0.980081187 0.011620769 0.008298044
#> 42 0.862690181 0.118982171 0.018327648
#> 43 0.972066116 0.015529010 0.012404874
#> 44 0.970044753 0.019718713 0.010236534
#> 45 0.972666471 0.017719155 0.009614374
#> 46 0.970366971 0.020516488 0.009116541
#> 47 0.976107170 0.014391734 0.009501095
#> 48 0.976775023 0.013414862 0.009810115
#> 49 0.977480970 0.013735143 0.008783886
#> 50 0.979715761 0.011823792 0.008460447
#> 51 0.018318604 0.951944296 0.029737101
#> 52 0.010814179 0.971238129 0.017947692
#> 53 0.014235046 0.891790350 0.093974603
#> 54 0.005786181 0.966940763 0.027273057
#> 55 0.009981970 0.928987453 0.061030577
#> 56 0.007241281 0.976824024 0.015934695
#> 57 0.011955157 0.931564878 0.056479964
#> 58 0.027493163 0.956082009 0.016424828
#> 59 0.011962197 0.978510679 0.009527124
#> 60 0.009012376 0.963524870 0.027462753
#> 61 0.026140270 0.944362832 0.029496897
#> 62 0.008960680 0.976508737 0.014530583
#> 63 0.014756004 0.978037671 0.007206325
#> 64 0.007555928 0.961425447 0.031018625
#> 65 0.016519963 0.979110217 0.004369820
#> 66 0.012760406 0.978269518 0.008970076
#> 67 0.010259358 0.948601270 0.041139372
#> 68 0.011301983 0.986193590 0.002504427
#> 69 0.017596811 0.860802783 0.121600406
#> 70 0.007125768 0.988219893 0.004654339
#> 71 0.013631797 0.642517908 0.343850295
#> 72 0.008551572 0.988178232 0.003270196
#> 73 0.012353356 0.731317504 0.256329140
#> 74 0.008682679 0.982436110 0.008881211
#> 75 0.010119364 0.985636977 0.004243658
#> 76 0.011151986 0.979573192 0.009274822
#> 77 0.016271956 0.935476057 0.048251986
#> 78 0.011905305 0.503904480 0.484190215
#> 79 0.007052957 0.949641723 0.043305320
#> 80 0.014345286 0.982401060 0.003253655
#> 81 0.007097995 0.986349971 0.006552034
#> 82 0.009280268 0.985924683 0.004795048
#> 83 0.008107154 0.988872557 0.003020289
#> 84 0.008331631 0.374992725 0.616675645
#> 85 0.013041126 0.937252819 0.049706055
#> 86 0.019812074 0.951701857 0.028486068
#> 87 0.011487159 0.944397029 0.044115812
#> 88 0.015781826 0.956525946 0.027692228
#> 89 0.015344577 0.979228642 0.005426780
#> 90 0.005303506 0.977116751 0.017579742
#> 91 0.006239145 0.977205153 0.016555702
#> 92 0.008360265 0.975084548 0.016555187
#> 93 0.006752410 0.988728546 0.004519044
#> 94 0.021261647 0.963825514 0.014912839
#> 95 0.006418065 0.981145148 0.012436787
#> 96 0.015835852 0.980150485 0.004013663
#> 97 0.009684800 0.984418166 0.005897033
#> 98 0.008881439 0.986746759 0.004371801
#> 99 0.026176864 0.962848535 0.010974601
#> 100 0.007872696 0.985856782 0.006270521
#> 101 0.012564527 0.004458707 0.982976766
#> 102 0.006659291 0.036804678 0.956536031
#> 103 0.007355960 0.007016778 0.985627262
#> 104 0.007836384 0.033590669 0.958572947
#> 105 0.007184046 0.002298249 0.990517706
#> 106 0.010542468 0.010305291 0.979152241
#> 107 0.011137133 0.456084652 0.532778215
#> 108 0.010077267 0.022801986 0.967120747
#> 109 0.011367477 0.037172141 0.951460383
#> 110 0.013342940 0.016031439 0.970625621
#> 111 0.010252540 0.097919622 0.891827838
#> 112 0.007242639 0.027283738 0.965473623
#> 113 0.007304733 0.009774552 0.982920715
#> 114 0.006482876 0.025931480 0.967585644
#> 115 0.009391357 0.002766218 0.987842425
#> 116 0.009363363 0.009971088 0.980665549
#> 117 0.009097188 0.066086962 0.924815849
#> 118 0.021021239 0.026406911 0.952571850
#> 119 0.022622967 0.026305317 0.951071715
#> 120 0.015209189 0.657834679 0.326956133
#> 121 0.007698807 0.005537345 0.986763847
#> 122 0.007959583 0.041821415 0.950219003
#> 123 0.016187041 0.021964512 0.961848447
#> 124 0.008721704 0.175318919 0.815959378
#> 125 0.008222188 0.017628576 0.974149235
#> 126 0.008977854 0.039158944 0.951863202
#> 127 0.008796262 0.249743319 0.741460419
#> 128 0.009994937 0.325602886 0.664402178
#> 129 0.006845761 0.003300807 0.989853432
#> 130 0.016375106 0.173516780 0.810108114
#> 131 0.012197980 0.026998506 0.960803514
#> 132 0.023222429 0.036843973 0.939933598
#> 133 0.007130579 0.002111755 0.990757666
#> 134 0.009833822 0.704336098 0.285830080
#> 135 0.011676744 0.415168905 0.573154351
#> 136 0.011933744 0.014502102 0.973564154
#> 137 0.012981516 0.015538600 0.971479885
#> 138 0.009661283 0.094681475 0.895657242
#> 139 0.010231223 0.401110856 0.588657921
#> 140 0.007725167 0.018493732 0.973781101
#> 141 0.008255178 0.002968125 0.988776697
#> 142 0.009201563 0.014578771 0.976219666
#> 143 0.006659291 0.036804678 0.956536031
#> 144 0.007624393 0.003695351 0.988680256
#> 145 0.010068929 0.005227319 0.984703752
#> 146 0.008412743 0.006454904 0.985132353
#> 147 0.008905518 0.067774097 0.923320385
#> 148 0.007996814 0.032552907 0.959450279
#> 149 0.013651104 0.033955240 0.952393656
#> 150 0.010194303 0.207351951 0.782453746
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 0.980215928 0.011332053 0.008452019
#> 2 0.972789042 0.018088424 0.009122534
#> 3 0.978921232 0.011963170 0.009115598
#> 4 0.974873935 0.015339580 0.009786485
#> 5 0.979415989 0.011685150 0.008898861
#> 6 0.973987337 0.016825176 0.009187487
#> 7 0.975640600 0.013556348 0.010803052
#> 8 0.980071585 0.011460510 0.008467905
#> 9 0.966107149 0.022557145 0.011335706
#> 10 0.976643730 0.014107166 0.009249105
#> 11 0.976427742 0.014367487 0.009204771
#> 12 0.978608835 0.012149616 0.009241549
#> 13 0.975184471 0.015571560 0.009243970
#> 14 0.969151779 0.017112238 0.013735983
#> 15 0.966809292 0.019503532 0.013687176
#> 16 0.958911041 0.023346524 0.017742434
#> 17 0.975694589 0.015019500 0.009285911
#> 18 0.979862314 0.011921041 0.008216645
#> 19 0.966824926 0.021926832 0.011248242
#> 20 0.976996325 0.013817662 0.009186013
#> 21 0.971151657 0.018010521 0.010837822
#> 22 0.977491378 0.013897364 0.008611258
#> 23 0.968401478 0.017130066 0.014468456
#> 24 0.966603037 0.022803530 0.010593434
#> 25 0.975391621 0.014523272 0.010085107
#> 26 0.967132928 0.022792285 0.010074787
#> 27 0.976660664 0.014599539 0.008739798
#> 28 0.979028141 0.012342753 0.008629107
#> 29 0.978915544 0.012256413 0.008828043
#> 30 0.976688025 0.013867875 0.009444100
#> 31 0.974838605 0.016007391 0.009154004
#> 32 0.969453503 0.019893930 0.010652567
#> 33 0.966623714 0.019453839 0.013922448
#> 34 0.968211197 0.018640805 0.013147998
#> 35 0.975654239 0.015405102 0.008940659
#> 36 0.979467014 0.011833785 0.008699201
#> 37 0.973152954 0.015973175 0.010873871
#> 38 0.977574392 0.012176202 0.010249405
#> 39 0.970609641 0.017951706 0.011438653
#> 40 0.979538261 0.011919047 0.008542692
#> 41 0.980314432 0.011336659 0.008348908
#> 42 0.864005696 0.117370962 0.018623341
#> 43 0.972247727 0.015142479 0.012609794
#> 44 0.970457075 0.019285982 0.010256944
#> 45 0.972988964 0.017306376 0.009704660
#> 46 0.970803918 0.020066409 0.009129673
#> 47 0.976317543 0.014013946 0.009668511
#> 48 0.977012851 0.013092241 0.009894908
#> 49 0.977733275 0.013400567 0.008866159
#> 50 0.979974113 0.011540684 0.008485203
#> 51 0.018160588 0.953456854 0.028382558
#> 52 0.010675537 0.972849423 0.016475040
#> 53 0.014050106 0.887791379 0.098158516
#> 54 0.005675146 0.968445399 0.025879455
#> 55 0.009859148 0.928435767 0.061705085
#> 56 0.007134364 0.978374023 0.014491614
#> 57 0.011802022 0.931450184 0.056747793
#> 58 0.026909241 0.957899235 0.015191523
#> 59 0.011827672 0.979825682 0.008346646
#> 60 0.008860267 0.965071909 0.026067824
#> 61 0.025685544 0.946106607 0.028207849
#> 62 0.008830505 0.978047068 0.013122427
#> 63 0.014560636 0.979191271 0.006248093
#> 64 0.007453452 0.962812569 0.029733978
#> 65 0.016303218 0.979844214 0.003852567
#> 66 0.012611634 0.979554990 0.007833376
#> 67 0.010120140 0.949560507 0.040319353
#> 68 0.011153340 0.986691758 0.002154901
#> 69 0.017400249 0.853232806 0.129366945
#> 70 0.006990670 0.989116647 0.003892683
#> 71 0.013588417 0.602212000 0.384199583
#> 72 0.008435320 0.988871289 0.002693392
#> 73 0.011836155 0.703716872 0.284446973
#> 74 0.008566074 0.983704020 0.007729905
#> 75 0.009994626 0.986462899 0.003542475
#> 76 0.011017559 0.980874380 0.008108061
#> 77 0.016125678 0.936001970 0.047872352
#> 78 0.011562330 0.449488622 0.538949048
#> 79 0.006950595 0.950423435 0.042625970
#> 80 0.014144735 0.982910865 0.002944400
#> 81 0.006943934 0.987461858 0.005594208
#> 82 0.009088079 0.986838491 0.004073430
#> 83 0.007984624 0.989525650 0.002489726
#> 84 0.008062204 0.315892367 0.676045429
#> 85 0.012874969 0.937689431 0.049435601
#> 86 0.019578768 0.953341457 0.027079775
#> 87 0.011357485 0.945167102 0.043475413
#> 88 0.015646401 0.958060629 0.026292970
#> 89 0.015141486 0.980168954 0.004689560
#> 90 0.005202443 0.978686298 0.016111260
#> 91 0.006134476 0.978764238 0.015101286
#> 92 0.008245308 0.976653366 0.015101326
#> 93 0.006641198 0.989600831 0.003757971
#> 94 0.020743571 0.965596562 0.013659867
#> 95 0.006312366 0.982592800 0.011094833
#> 96 0.015640797 0.980853425 0.003505778
#> 97 0.009538884 0.985449531 0.005011585
#> 98 0.008761746 0.987594793 0.003643462
#> 99 0.025683328 0.964269888 0.010046785
#> 100 0.007745888 0.986923310 0.005330801
#> 101 0.011992414 0.002870515 0.985137071
#> 102 0.006211105 0.023262634 0.970526261
#> 103 0.006983003 0.004044552 0.988972445
#> 104 0.007405371 0.021090787 0.971503842
#> 105 0.006840404 0.001338103 0.991821493
#> 106 0.009963051 0.006357922 0.983679027
#> 107 0.011016558 0.398756665 0.590226777
#> 108 0.009631951 0.014103930 0.976264119
#> 109 0.009749847 0.024164819 0.966085334
#> 110 0.012669027 0.010014508 0.977316465
#> 111 0.010025847 0.067946331 0.922027822
#> 112 0.006871605 0.016831345 0.976297050
#> 113 0.006960722 0.005603858 0.987435421
#> 114 0.006138556 0.015899833 0.977961611
#> 115 0.008959381 0.001664402 0.989376217
#> 116 0.008939192 0.005763879 0.985296929
#> 117 0.008261590 0.044273638 0.947464772
#> 118 0.018979540 0.017366527 0.963653932
#> 119 0.020456303 0.017434114 0.962109583
#> 120 0.014810547 0.620020053 0.365169399
#> 121 0.007316625 0.003189893 0.989493483
#> 122 0.007443956 0.026777336 0.965778708
#> 123 0.013823099 0.014784426 0.971392475
#> 124 0.008249597 0.131067155 0.860683248
#> 125 0.007858695 0.010525486 0.981615819
#> 126 0.008450033 0.025092098 0.966457869
#> 127 0.008446739 0.196527177 0.795026084
#> 128 0.009751277 0.267605950 0.722642774
#> 129 0.006515611 0.001845654 0.991638734
#> 130 0.014172963 0.130262290 0.855564747
#> 131 0.010271911 0.017556837 0.972171253
#> 132 0.020695112 0.024447257 0.954857631
#> 133 0.006789243 0.001244114 0.991966643
#> 134 0.009699524 0.672082721 0.318217755
#> 135 0.011184723 0.356710701 0.632104576
#> 136 0.011309390 0.009020289 0.979670321
#> 137 0.012417107 0.009370017 0.978212876
#> 138 0.009563389 0.065413366 0.925023245
#> 139 0.010066642 0.342188679 0.647744679
#> 140 0.007378365 0.011080466 0.981541169
#> 141 0.007859891 0.001794524 0.990345585
#> 142 0.008787542 0.008637634 0.982574824
#> 143 0.006211105 0.023262634 0.970526261
#> 144 0.007249980 0.002164716 0.990585304
#> 145 0.009584369 0.003169843 0.987245788
#> 146 0.008012509 0.003691416 0.988296075
#> 147 0.008015049 0.045544020 0.946440930
#> 148 0.007572085 0.020391313 0.972036603
#> 149 0.013113724 0.021518160 0.965368116
#> 150 0.009856472 0.158724753 0.831418775