SVM {structToolbox} | R Documentation

## Support Vector Machine Classifier

### Description

Support Vector Machines (SVM) are a machine learning algorithm for classification. They can make use of kernel functions to generate highly non-linear boundaries between groups.

### Usage

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

### Arguments

`factor_name` | (character) The name of a sample-meta column to use.
`kernel` | (character) Kernel type. Allowed values are limited to the following: `"linear"`: . `"polynomial"`: . `"radial"`: . `"sigmoid"`: . The default is `"linear"`.
`degree` | (numeric) The polynomial degree. The default is `3`.
`gamma` | (numeric) The gamma parameter. The default is `1`.
`coef0` | (numeric) The offset coefficient. The default is `0`.
`cost` | (numeric) The cost of violating the constraints. The default is `1`.
`class_weights` | (numeric, character, NULL) A named vector of weights for the different classes. Specifying "inverse" will choose the weights inversely proportional to the class distribution. The default is `NULL`.
`...` | Additional slots and values passed to `struct_class`.

### Details

This object makes use of functionality from the following packages:

- `e1071`

### Value

A `SVM` object.

struct object

### References

Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F (2019). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-3, https://CRAN.R-project.org/package=e1071.

Brereton RG, Lloyd GR (2010). “Support Vector Machines for classification and regression.” The Analyst, 135(2), 230-267.

### Examples

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

[Package structToolbox version 1.2.0 Index]