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, ...)
logical vector indicating the variables to be scaled.
type of support vector machine.
kernel function used in training and predicting.
list of hyper-parameters (kernel parameters).
cost of constraints violation defined as the regularization term in the Lagrange formulation.
parameter needed for nu-svc, one-svc, and nu-svr.
parameter in the insensitive-loss function used for eps-svr, nu-svr and eps-bsvm.
cache memory in MB.
tolerance of termination criterion.
whether to use the shrinking-heuristics.
inverse kernel width used by the ANOVA, Bessel, and Laplacian kernels.
degree of the ANOVA, Bessel, and polynomial kernel functions.
arguments passed to SVMModel.
order of the Bessel function to be used as a kernel.
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 used in polynomial and hyperbolic tangent kernels.
MLModel class object.
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.
# NOT RUN {
fit(sale_amount ~ ., data = ICHomes, model = SVMRadialModel)
# }
Run the code above in your browser using DataLab