Unified (formula-based) interface version of the support vector machine
algorithm provided by e1071::svm().
Usage
mlSvm(train, ...)
ml_svm(train, ...)
# S3 method for formula
mlSvm(
formula,
data,
scale = TRUE,
type = NULL,
kernel = "radial",
classwt = NULL,
...,
subset,
na.action
)
# S3 method for default
mlSvm(
train,
response,
scale = TRUE,
type = NULL,
kernel = "radial",
classwt = NULL,
...
)
# S3 method for mlSvm
predict(
object,
newdata,
type = c("class", "membership", "both"),
method = c("direct", "cv"),
na.action = na.exclude,
...
)Arguments
- train
a matrix or data frame with predictors.
- ...
further arguments passed to the classification or regression method. See
e1071::svm().- formula
a formula with left term being the factor variable to predict (for supervised classification), a vector of numbers (for regression) or nothing (for unsupervised classification) and the right term with the list of independent, predictive variables, separated with a plus sign. If the data frame provided contains only the dependent and independent variables, one can use the
class ~ .short version (that one is strongly encouraged). Variables with minus sign are eliminated. Calculations on variables are possible according to usual formula convention (possibly protected by usingI()).- data
a data.frame to use as a training set.
- scale
are the variables scaled (so that mean = 0 and standard deviation = 1)?
TRUEby default. If a vector is provided, it is applied to variables with recycling.- type
For
ml_svm()/mlSvm(), the type of classification or regression machine to use. The default value ofNULLuses"C-classification"if response variable is factor andeps-regressionif it is numeric. It can also be"nu-classification"or"nu-regression". The "C" and "nu" versions are basically the same but with a different parameterisation. The range of C is from zero to infinity, while the range for nu is from zero to one. A fifth option is"one_classification"that is specific to novelty detection (find the items that are different from the rest). Forpredict(), the type of prediction to return."class"by default, the predicted classes. Other options are"membership"the membership (number between 0 and 1) to the different classes, or"both"to return classes and memberships.- kernel
the kernel used by svm, see
e1071::svm()for further explanations. Can be"radial","linear","polynomial"or"sigmoid".- classwt
priors of the classes. Need not add up to one.
- subset
index vector with the cases to define the training set in use (this argument must be named, if provided).
- na.action
function to specify the action to be taken if
NAs are found. Forml_svm()na.failis used by default. The calculation is stopped if there is anyNAin the data. Another option isna.omit, where cases with missing values on any required variable are dropped (this argument must be named, if provided). For thepredict()method, the default, and most suitable option, isna.exclude. In that case, rows withNAs innewdata=are excluded from prediction, but reinjected in the final results so that the number of items is still the same (and in the same order asnewdata=).- response
a vector of factor (classification) or numeric (regression).
- object
an mlSvm object
- newdata
a new dataset with same conformation as the training set (same variables, except may by the class for classification or dependent variable for regression). Usually a test set, or a new dataset to be predicted.
- method
"direct"(default) or"cv"."direct"predicts new cases innewdata=if this argument is provided, or the cases in the training set if not. Take care that not providingnewdata=means that you just calculate the self-consistency of the classifier but cannot use the metrics derived from these results for the assessment of its performances. Either use a different data set innewdata=or use the alternate cross-validation ("cv") technique. If you specifymethod = "cv"thencvpredict()is used and you cannot providenewdata=in that case.
Value
ml_svm()/mlSvm() creates an mlSvm, mlearning object
containing the classifier and a lot of additional metadata used by the
functions and methods you can apply to it like predict() or
cvpredict(). In case you want to program new functions or extract
specific components, inspect the "unclassed" object using unclass().
See also
mlearning(), cvpredict(), confusion(), also e1071::svm()
that actually does the calculation.
Examples
# Prepare data: split into training set (2/3) and test set (1/3)
data("iris", package = "datasets")
train <- c(1:34, 51:83, 101:133)
iris_train <- iris[train, ]
iris_test <- iris[-train, ]
# One case with missing data in train set, and another case in test set
iris_train[1, 1] <- NA
iris_test[25, 2] <- NA
iris_svm <- ml_svm(data = iris_train, Species ~ .)
summary(iris_svm)
#> A mlearning object of class mlSvm (support vector machine):
#> Initial call: mlSvm.formula(formula = Species ~ ., data = iris_train)
#>
#> Call:
#> svm.default(x = sapply(train, as.numeric), y = response, scale = scale,
#> type = type, kernel = kernel, class.weights = classwt, probability = TRUE,
#> .args. = ..1)
#>
#>
#> Parameters:
#> SVM-Type: C-classification
#> SVM-Kernel: radial
#> cost: 1
#>
#> Number of Support Vectors: 42
#>
#> ( 8 17 17 )
#>
#>
#> Number of Classes: 3
#>
#> Levels:
#> setosa versicolor virginica
#>
#>
#>
predict(iris_svm) # Default type is class
#> [1] setosa setosa setosa setosa setosa setosa
#> [7] setosa setosa setosa setosa setosa setosa
#> [13] setosa setosa setosa setosa setosa setosa
#> [19] setosa setosa setosa setosa setosa setosa
#> [25] setosa setosa setosa setosa setosa setosa
#> [31] setosa setosa setosa versicolor versicolor versicolor
#> [37] versicolor versicolor versicolor versicolor versicolor versicolor
#> [43] versicolor versicolor versicolor versicolor versicolor versicolor
#> [49] versicolor versicolor versicolor versicolor versicolor versicolor
#> [55] versicolor versicolor versicolor versicolor versicolor versicolor
#> [61] virginica versicolor versicolor versicolor versicolor versicolor
#> [67] virginica virginica virginica virginica virginica virginica
#> [73] virginica virginica virginica virginica virginica virginica
#> [79] virginica virginica virginica virginica virginica virginica
#> [85] virginica versicolor virginica virginica virginica virginica
#> [91] virginica virginica virginica virginica virginica virginica
#> [97] virginica virginica virginica
#> Levels: setosa versicolor virginica
predict(iris_svm, type = "membership")
#> setosa versicolor virginica
#> 1 0.952774913 0.030871412 0.016353675
#> 2 0.968246698 0.017814836 0.013938466
#> 3 0.962422695 0.022536988 0.015040318
#> 4 0.964604420 0.019090493 0.016305087
#> 5 0.952920314 0.028915268 0.018164418
#> 6 0.964235804 0.019066326 0.016697870
#> 7 0.967382674 0.018700065 0.013917261
#> 8 0.940953600 0.037583456 0.021462944
#> 9 0.962462310 0.022665107 0.014872582
#> 10 0.957392104 0.025273315 0.017334581
#> 11 0.967071625 0.018274876 0.014653499
#> 12 0.957854393 0.025941969 0.016203638
#> 13 0.951230899 0.027373121 0.021395980
#> 14 0.938694244 0.036062239 0.025243518
#> 15 0.930932398 0.037660337 0.031407266
#> 16 0.953219533 0.028252123 0.018528344
#> 17 0.965037235 0.020515079 0.014447686
#> 18 0.942181745 0.037607045 0.020211210
#> 19 0.956476189 0.025217590 0.018306221
#> 20 0.951143415 0.031490947 0.017365638
#> 21 0.959398293 0.023903159 0.016698548
#> 22 0.951029511 0.024514896 0.024455593
#> 23 0.944831578 0.038361661 0.016806761
#> 24 0.962832146 0.021532656 0.015635198
#> 25 0.943963470 0.038356821 0.017679709
#> 26 0.961153875 0.024412795 0.014433330
#> 27 0.963690404 0.021316634 0.014992962
#> 28 0.964058935 0.021234779 0.014706286
#> 29 0.965595320 0.020291904 0.014112777
#> 30 0.960520933 0.024951684 0.014527383
#> 31 0.946807644 0.035864733 0.017327624
#> 32 0.940206660 0.032229594 0.027563746
#> 33 0.943120577 0.031535798 0.025343625
#> 34 0.020963596 0.915961853 0.063074551
#> 35 0.016993047 0.952505456 0.030501498
#> 36 0.017671232 0.819575341 0.162753427
#> 37 0.009421227 0.950487262 0.040091511
#> 38 0.010969701 0.896696242 0.092334057
#> 39 0.010661393 0.968091338 0.021247269
#> 40 0.021361907 0.899152303 0.079485790
#> 41 0.029940743 0.947635850 0.022423407
#> 42 0.012796178 0.969508994 0.017694828
#> 43 0.014664266 0.944933707 0.040402026
#> 44 0.024259722 0.934219500 0.041520778
#> 45 0.013588403 0.964252771 0.022158826
#> 46 0.018468182 0.969363152 0.012168667
#> 47 0.010214462 0.949759268 0.040026270
#> 48 0.023092401 0.969530046 0.007377553
#> 49 0.015699093 0.965414193 0.018886714
#> 50 0.016055557 0.929710698 0.054233745
#> 51 0.015149669 0.980865568 0.003984763
#> 52 0.019918527 0.787181492 0.192899980
#> 53 0.011021435 0.981596035 0.007382529
#> 54 0.022867705 0.619562645 0.357569650
#> 55 0.010899323 0.983325760 0.005774916
#> 56 0.013576732 0.678881884 0.307541384
#> 57 0.010787973 0.976565011 0.012647017
#> 58 0.011807199 0.980322599 0.007870202
#> 59 0.013159217 0.968493106 0.018347677
#> 60 0.014841249 0.898495677 0.086663074
#> 61 0.015575825 0.423357433 0.561066741
#> 62 0.010068025 0.934621023 0.055310951
#> 63 0.019267710 0.975995279 0.004737011
#> 64 0.011135553 0.978660908 0.010203539
#> 65 0.013676848 0.978899513 0.007423639
#> 66 0.011502970 0.983392125 0.005104905
#> 67 0.019235585 0.006796298 0.973968117
#> 68 0.010494832 0.046342605 0.943162563
#> 69 0.010480653 0.005983146 0.983536201
#> 70 0.011887374 0.044459946 0.943652680
#> 71 0.010915737 0.003342065 0.985742198
#> 72 0.013081741 0.007100951 0.979817308
#> 73 0.015892993 0.392434228 0.591672779
#> 74 0.013193374 0.015848064 0.970958562
#> 75 0.013684208 0.030398101 0.955917690
#> 76 0.019045733 0.015013759 0.965940508
#> 77 0.016725909 0.096380607 0.886893484
#> 78 0.010731795 0.029645356 0.959622849
#> 79 0.011009667 0.009681532 0.979308801
#> 80 0.010060303 0.027605123 0.962334574
#> 81 0.014098795 0.004140644 0.981760561
#> 82 0.015411488 0.012837844 0.971750668
#> 83 0.013296118 0.073259072 0.913444810
#> 84 0.024332649 0.023191460 0.952475891
#> 85 0.023234439 0.016001691 0.960763869
#> 86 0.018080835 0.572952116 0.408967049
#> 87 0.011863861 0.005932962 0.982203177
#> 88 0.012470468 0.050151824 0.937377708
#> 89 0.017177365 0.013410635 0.969412000
#> 90 0.012209514 0.170174585 0.817615901
#> 91 0.013586337 0.019251949 0.967161714
#> 92 0.012729685 0.030355806 0.956914509
#> 93 0.012665785 0.251274790 0.736059425
#> 94 0.015233431 0.338331853 0.646434716
#> 95 0.010240692 0.004450885 0.985308423
#> 96 0.017185457 0.118323789 0.864490754
#> 97 0.013799572 0.016625171 0.969575258
#> 98 0.025818577 0.031130094 0.943051329
#> 99 0.010528869 0.002922124 0.986549007
predict(iris_svm, type = "both")
#> $class
#> [1] setosa setosa setosa setosa setosa setosa
#> [7] setosa setosa setosa setosa setosa setosa
#> [13] setosa setosa setosa setosa setosa setosa
#> [19] setosa setosa setosa setosa setosa setosa
#> [25] setosa setosa setosa setosa setosa setosa
#> [31] setosa setosa setosa versicolor versicolor versicolor
#> [37] versicolor versicolor versicolor versicolor versicolor versicolor
#> [43] versicolor versicolor versicolor versicolor versicolor versicolor
#> [49] versicolor versicolor versicolor versicolor versicolor versicolor
#> [55] versicolor versicolor versicolor versicolor versicolor versicolor
#> [61] virginica versicolor versicolor versicolor versicolor versicolor
#> [67] virginica virginica virginica virginica virginica virginica
#> [73] virginica virginica virginica virginica virginica virginica
#> [79] virginica virginica virginica virginica virginica virginica
#> [85] virginica versicolor virginica virginica virginica virginica
#> [91] virginica virginica virginica virginica virginica virginica
#> [97] virginica virginica virginica
#> Levels: setosa versicolor virginica
#>
#> $membership
#> setosa versicolor virginica
#> 1 0.952774913 0.030871412 0.016353675
#> 2 0.968246698 0.017814836 0.013938466
#> 3 0.962422695 0.022536988 0.015040318
#> 4 0.964604420 0.019090493 0.016305087
#> 5 0.952920314 0.028915268 0.018164418
#> 6 0.964235804 0.019066326 0.016697870
#> 7 0.967382674 0.018700065 0.013917261
#> 8 0.940953600 0.037583456 0.021462944
#> 9 0.962462310 0.022665107 0.014872582
#> 10 0.957392104 0.025273315 0.017334581
#> 11 0.967071625 0.018274876 0.014653499
#> 12 0.957854393 0.025941969 0.016203638
#> 13 0.951230899 0.027373121 0.021395980
#> 14 0.938694244 0.036062239 0.025243518
#> 15 0.930932398 0.037660337 0.031407266
#> 16 0.953219533 0.028252123 0.018528344
#> 17 0.965037235 0.020515079 0.014447686
#> 18 0.942181745 0.037607045 0.020211210
#> 19 0.956476189 0.025217590 0.018306221
#> 20 0.951143415 0.031490947 0.017365638
#> 21 0.959398293 0.023903159 0.016698548
#> 22 0.951029511 0.024514896 0.024455593
#> 23 0.944831578 0.038361661 0.016806761
#> 24 0.962832146 0.021532656 0.015635198
#> 25 0.943963470 0.038356821 0.017679709
#> 26 0.961153875 0.024412795 0.014433330
#> 27 0.963690404 0.021316634 0.014992962
#> 28 0.964058935 0.021234779 0.014706286
#> 29 0.965595320 0.020291904 0.014112777
#> 30 0.960520933 0.024951684 0.014527383
#> 31 0.946807644 0.035864733 0.017327624
#> 32 0.940206660 0.032229594 0.027563746
#> 33 0.943120577 0.031535798 0.025343625
#> 34 0.020963596 0.915961853 0.063074551
#> 35 0.016993047 0.952505456 0.030501498
#> 36 0.017671232 0.819575341 0.162753427
#> 37 0.009421227 0.950487262 0.040091511
#> 38 0.010969701 0.896696242 0.092334057
#> 39 0.010661393 0.968091338 0.021247269
#> 40 0.021361907 0.899152303 0.079485790
#> 41 0.029940743 0.947635850 0.022423407
#> 42 0.012796178 0.969508994 0.017694828
#> 43 0.014664266 0.944933707 0.040402026
#> 44 0.024259722 0.934219500 0.041520778
#> 45 0.013588403 0.964252771 0.022158826
#> 46 0.018468182 0.969363152 0.012168667
#> 47 0.010214462 0.949759268 0.040026270
#> 48 0.023092401 0.969530046 0.007377553
#> 49 0.015699093 0.965414193 0.018886714
#> 50 0.016055557 0.929710698 0.054233745
#> 51 0.015149669 0.980865568 0.003984763
#> 52 0.019918527 0.787181492 0.192899980
#> 53 0.011021435 0.981596035 0.007382529
#> 54 0.022867705 0.619562645 0.357569650
#> 55 0.010899323 0.983325760 0.005774916
#> 56 0.013576732 0.678881884 0.307541384
#> 57 0.010787973 0.976565011 0.012647017
#> 58 0.011807199 0.980322599 0.007870202
#> 59 0.013159217 0.968493106 0.018347677
#> 60 0.014841249 0.898495677 0.086663074
#> 61 0.015575825 0.423357433 0.561066741
#> 62 0.010068025 0.934621023 0.055310951
#> 63 0.019267710 0.975995279 0.004737011
#> 64 0.011135553 0.978660908 0.010203539
#> 65 0.013676848 0.978899513 0.007423639
#> 66 0.011502970 0.983392125 0.005104905
#> 67 0.019235585 0.006796298 0.973968117
#> 68 0.010494832 0.046342605 0.943162563
#> 69 0.010480653 0.005983146 0.983536201
#> 70 0.011887374 0.044459946 0.943652680
#> 71 0.010915737 0.003342065 0.985742198
#> 72 0.013081741 0.007100951 0.979817308
#> 73 0.015892993 0.392434228 0.591672779
#> 74 0.013193374 0.015848064 0.970958562
#> 75 0.013684208 0.030398101 0.955917690
#> 76 0.019045733 0.015013759 0.965940508
#> 77 0.016725909 0.096380607 0.886893484
#> 78 0.010731795 0.029645356 0.959622849
#> 79 0.011009667 0.009681532 0.979308801
#> 80 0.010060303 0.027605123 0.962334574
#> 81 0.014098795 0.004140644 0.981760561
#> 82 0.015411488 0.012837844 0.971750668
#> 83 0.013296118 0.073259072 0.913444810
#> 84 0.024332649 0.023191460 0.952475891
#> 85 0.023234439 0.016001691 0.960763869
#> 86 0.018080835 0.572952116 0.408967049
#> 87 0.011863861 0.005932962 0.982203177
#> 88 0.012470468 0.050151824 0.937377708
#> 89 0.017177365 0.013410635 0.969412000
#> 90 0.012209514 0.170174585 0.817615901
#> 91 0.013586337 0.019251949 0.967161714
#> 92 0.012729685 0.030355806 0.956914509
#> 93 0.012665785 0.251274790 0.736059425
#> 94 0.015233431 0.338331853 0.646434716
#> 95 0.010240692 0.004450885 0.985308423
#> 96 0.017185457 0.118323789 0.864490754
#> 97 0.013799572 0.016625171 0.969575258
#> 98 0.025818577 0.031130094 0.943051329
#> 99 0.010528869 0.002922124 0.986549007
#>
# Self-consistency, do not use for assessing classifier performances!
confusion(iris_svm)
#> 99 items classified with 97 true positives (error rate = 2%)
#> Predicted
#> Actual 01 02 03 (sum) (FNR%)
#> 01 setosa 33 0 0 33 0
#> 02 versicolor 0 32 1 33 3
#> 03 virginica 0 1 32 33 3
#> (sum) 33 33 33 99 2
# Use an independent test set instead
confusion(predict(iris_svm, newdata = iris_test), iris_test$Species)
#> 50 items classified with 47 true positives (error rate = 6%)
#> Predicted
#> Actual 01 02 03 04 (sum) (FNR%)
#> 01 setosa 16 0 0 0 16 0
#> 02 NA 0 0 0 0 0
#> 03 versicolor 0 1 15 1 17 12
#> 04 virginica 0 0 1 16 17 6
#> (sum) 16 1 16 17 50 6
# Another dataset
data("HouseVotes84", package = "mlbench")
house_svm <- ml_svm(data = HouseVotes84, Class ~ ., na.action = na.omit)
summary(house_svm)
#> A mlearning object of class mlSvm (support vector machine):
#> Initial call: mlSvm.formula(formula = Class ~ ., data = HouseVotes84, na.action = na.omit)
#>
#> Call:
#> svm.default(x = sapply(train, as.numeric), y = response, scale = scale,
#> type = type, kernel = kernel, class.weights = classwt, probability = TRUE,
#> .args. = ..1)
#>
#>
#> Parameters:
#> SVM-Type: C-classification
#> SVM-Kernel: radial
#> cost: 1
#>
#> Number of Support Vectors: 78
#>
#> ( 43 35 )
#>
#>
#> Number of Classes: 2
#>
#> Levels:
#> democrat republican
#>
#>
#>
# Cross-validated confusion matrix
confusion(cvpredict(house_svm), na.omit(HouseVotes84)$Class)
#> 232 items classified with 224 true positives (error rate = 3.4%)
#> Predicted
#> Actual 01 02 (sum) (FNR%)
#> 01 democrat 118 6 124 5
#> 02 republican 2 106 108 2
#> (sum) 120 112 232 3
# Regression using support vector machine
data(airquality, package = "datasets")
ozone_svm <- ml_svm(data = airquality, Ozone ~ ., na.action = na.omit)
summary(ozone_svm)
#> A mlearning object of class mlSvm (support vector machine):
#> [regression variant]
#> Initial call: mlSvm.formula(formula = Ozone ~ ., data = airquality, na.action = na.omit)
#>
#> Call:
#> svm.default(x = sapply(train, as.numeric), y = response, scale = scale,
#> type = type, kernel = kernel, class.weights = classwt, probability = TRUE,
#> .args. = ..1)
#>
#>
#> Parameters:
#> SVM-Type: eps-regression
#> SVM-Kernel: radial
#> cost: 1
#> gamma: 0.2
#> epsilon: 0.1
#>
#> Sigma: 0.3644775
#>
#>
#> Number of Support Vectors: 90
#>
#>
#>
#>
#>
plot(na.omit(airquality)$Ozone, predict(ozone_svm))
abline(a = 0, b = 1)
