SVMModel: Support Vector Machine Models

In MachineShop: Machine Learning Models and Tools

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 = character(),
  kernel = c("rbfdot", "polydot", "vanilladot", "tanhdot", "laplacedot", "besseldot",
    "anovadot", "splinedot"),
  kpar = "automatic",
  C = 1,
  nu = 0.2,
  epsilon = 0.1,
  prob.model = FALSE,
  cache = 40,
  tol = 0.001,
  shrinking = TRUE
)

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

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

SVMLaplaceModel(sigma = numeric(), ...)

SVMLinearModel(...)

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

SVMRadialModel(sigma = numeric(), ...)

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.
prob.model	logical indicating whether to calculate the scaling parameter of the Laplacian distribution fitted on the residuals of numeric response variables. Ignored in the case of a factor response variable.
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 from the other constructors.
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:

• SVMModel: NULL

• SVMANOVAModel: C, degree

• SVMBesselModel: C, order, degree

• SVMLaplaceModel: C, sigma

• SVMLinearModel: C

• SVMPolyModel: C, degree, scale

• SVMRadialModel: C, sigma

The kernel-specific constructor functions SVMANOVAModel, SVMBesselModel, SVMLaplaceModel, SVMLinearModel, SVMPolyModel, SVMRadialModel, SVMSplineModel, and SVMTanhModel are special cases of SVMModel which automatically set its kernel and kpar arguments. These are called directly in typical usage unless SVMModel is needed to specify a more general model.

Default argument values and further model details can be found in the source See Also link below.

Value

MLModel class object.

See Also

ksvm, fit, resample

Examples

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

MachineShop documentation built on Sept. 11, 2024, 6:28 p.m.