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 = 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, ...)

Value

MLModel class object.

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.

Examples

Run this code

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

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