svm_classifier {autoMrP}  R Documentation 
svm_classifier
applies support vector machine classification to a
data set.
svm_classifier( form, data, kernel, type, probability, svm.gamma, svm.cost, verbose = c(TRUE, FALSE) )
form 
Model formula. A twosided linear formula describing the model to be fit, with the outcome on the LHS and the covariates separated by + operators on the RHS. 
data 
Data. A data.frame containing the crossvalidation data used to train and evaluate the model. 
kernel 
Kernel for SVM. A character string specifying the kernel to be used for SVM. The possible types are linear, polynomial, radial, and sigmoid. Default is radial. 
type 
svm can be used as a classification machine, as a regression machine, or for novelty detection. Depending of whether y is a factor or not, the default setting for type is Cclassification or epsregression, respectively, but may be overwritten by setting an explicit value. Valid options are: #'

probability 
Probability predictions. A logical argument indicating whether the model should allow for probability predictions 
svm.gamma 
Gamma parameter for SVM. This parameter is needed for all kernels except linear. 
svm.cost 
Cost parameter for SVM. This parameter specifies the cost of constraints violation. 
verbose 
Verbose output. A logical vector indicating whether or not verbose output should be printed. 
The support vector machine model. An svm
object.