SVM {structToolbox} | R Documentation

## SVM model classifier

### Description

Support vector machines model classifier. Wraps svm from the "e1071" package, which interfaces with the "libsvm" library to train SVM classifiers.

### Usage

    SVM(
      factor_name,
      kernel = "linear",
      degree = 3,
      gamma = 1,
      coef0 = 0,
      cost = 1,
      class_weights = NULL,
      ...
    )

### Arguments

`factor_name` | The sample-meta column name to use for group labels
`kernel` | the kernel used in training and predicting. You might consider changing some of the following parameters, depending on the kernel type. linear: u'*v polynomial: (gamma*u'*v + coef0)^degree radial basis: exp(-gamma*|u-v|^2) sigmoid: tanh(gamma*u'*v + coef0)
`degree` | parameter needed for kernel of type `polynomial` (default: 3)
`gamma` | parameter needed for all kernels except `linear` (default: 1/(data dimension))
`coef0` | parameter needed for kernels of type `polynomial` and `sigmoid` (default: 0)
`cost` | cost of constraints violation (default: 1)—it is the ‘C’-constant of the regularization term in the Lagrange formulation.
`class_weights` | a named vector of weights for the different classes, used for asymmetric class sizes. Not all factor levels have to be supplied (default weight: 1). All components have to be named. Specifying "inverse" will choose the weights inversely proportional to the class distribution.
`...` | additional slots and values passed to struct_class

### Value

struct object

### Examples

    M = SVM(factor_name='Species',gamma=1)

[Package structToolbox version 0.99.8 Index]