Support Vector Machine Classifier

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.

Value

A SVM object with the following output slots:

SV	(matrix)
index	(numeric)
coefs	(matrix)
pred	(data.frame)
decision_values	(data.frame)

struct object

Details

This object makes use of functionality from the following packages:

• e1071

Inheritance

A SVM object inherits the following struct classes:

[SVM] >> [model] >> [struct_class]

References

Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F (2023). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-14, 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 = "V1",
      kernel = "linear",
      degree = 3,
      gamma = 1,
      coef0 = 0,
      cost = 1,
      class_weights = 1)

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