svm_classifier {autoMrP}

SVM classifier

Description

svm_classifier applies support vector machine classification to a data set.

Usage

svm_classifier(
  y,
  form,
  data,
  kernel,
  type,
  probability,
  svm.gamma,
  svm.cost,
  verbose = c(TRUE, FALSE)
)

Arguments

y

Outcome variable. A character vector containing the column names of the outcome variable. A character scalar containing the column name of the outcome variable in survey.

form

Model formula. A two-sided 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 cross-validation 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 C-classification or eps-regression, respectively, but may be overwritten by setting an explicit value. Valid options are: #'

1. C-classification

2. nu-classification

3. one-classification (for novelty detection)

4. eps-regression

5. nu-regression

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.

Value

The support vector machine model. An svm object.