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,
  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.
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:

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 and further model details can be found in the source link below.

Examples

Run this code

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