SVM {structToolbox} | R Documentation |
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.
SVM( factor_name, kernel = "linear", degree = 3, gamma = 1, coef0 = 0, cost = 1, class_weights = NULL, ... )
factor_name |
(character) The name of a sample-meta column to use. |
kernel |
(character) Kernel type. Allowed values are limited to the following:
The default is |
degree |
(numeric) The polynomial degree. The default is |
gamma |
(numeric) The gamma parameter. The default is |
coef0 |
(numeric) The offset coefficient. The default is |
cost |
(numeric) The cost of violating the constraints. The default is |
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 |
... |
Additional slots and values passed to |
This object makes use of functionality from the following packages:
e1071
A SVM
object.
struct object
Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F (2021). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-6, 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.
M = SVM(factor_name='Species',gamma=1)