SVMModel {MachineShop} | R Documentation |
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.
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, ...)
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 |
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. |
factor
, numeric
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.
MLModel
class object.
fit(sale_amount ~ ., data = ICHomes, model = SVMRadialModel)