MachineShop: SVMModel – R documentation

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SVMModel

Support Vector Machine Models

Description

Fits the well known C-svc, nu-svc, (classification) one-class-svc (novelty) eps-svr, nu-svr (regression) formulations along with native multi-class classification formulations and the bound-constraint SVM formulations.

Usage

SVMModel(
  scaled = TRUE,
  type = NULL,
  kernel = c("rbfdot", "polydot", "vanilladot", "tanhdot", "laplacedot", "besseldot",
    "anovadot", "splinedot"),
  kpar = "automatic",
  C = 1,
  nu = 0.2,
  epsilon = 0.1,
  cache = 40,
  tol = 0.001,
  shrinking = TRUE
)

SVMANOVAModel(sigma = 1, degree = 1, ...)

SVMBesselModel(sigma = 1, order = 1, degree = 1, ...)

SVMLaplaceModel(sigma = NULL, ...)

SVMLinearModel(...)

SVMPolyModel(degree = 1, scale = 1, offset = 1, ...)

SVMRadialModel(sigma = NULL, ...)

SVMSplineModel(...)

SVMTanhModel(scale = 1, offset = 1, ...)

Arguments

`scaled`	logical vector indicating the variables to be scaled.
`type`	type of support vector machine.
`kernel`	kernel function used in training and predicting.
`kpar`	list of hyper-parameters (kernel parameters).
`C`	cost of constraints violation defined as the regularization term in the Lagrange formulation.
`nu`	parameter needed for nu-svc, one-svc, and nu-svr.
`epsilon`	parameter in the insensitive-loss function used for eps-svr, nu-svr and eps-bsvm.
`cache`	cache memory in MB.
`tol`	tolerance of termination criterion.
`shrinking`	whether to use the shrinking-heuristics.
`sigma`	inverse kernel width used by the ANOVA, Bessel, and Laplacian kernels.
`degree`	degree of the ANOVA, Bessel, and polynomial kernel functions.
`...`	arguments passed to `SVMModel`.
`order`	order of the Bessel function to be used as a kernel.
`scale`	scaling parameter of the polynomial and hyperbolic tangent kernels as a convenient way of normalizing patterns without the need to modify the data itself.
`offset`	offset used in polynomial and hyperbolic tangent kernels.

Details

Response Types:

factor, numeric

Automatic Tuning of Grid Parameters

SVMANOVAModel: C, degree
SVMBesselModel: C, order, degree
SVMLaplaceModel: C, sigma
SVMLinearModel: C
SVMPolyModel: C, degree, scale
SVMRadialModel: C, sigma

Arguments kernel and kpar are automatically set by the kernel-specific constructor functions. Default values for the NULL arguments and further model details can be found in the source link below.

Value

MLModel class object.

Examples

fit(sale_amount ~ ., data = ICHomes, model = SVMRadialModel)

MachineShop

Machine Learning Models and Tools

v2.8.0

GPL-3

Authors

Brian J Smith [aut, cre]

Initial release

2021-04-16