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Parsnip model specs: epsilon-SVR with MAPE loss (Model 1)

psvr_mape_specs.Rd

Create parsnip model specifications for psvr_mape() with a fixed kernel type. Kernel parameters are tunable parsnip arguments; the symmetry parameter a and solver tolerance are engine arguments passed via set_engine().

Usage

psvr_mape_rbf(
  mode = "regression",
  engine = "psvr",
  cost = NULL,
  margin = NULL,
  rbf_sigma = NULL,
  sym_type = NULL
)

psvr_mape_poly(
  mode = "regression",
  engine = "psvr",
  cost = NULL,
  margin = NULL,
  degree = NULL,
  scale_factor = NULL,
  sym_type = NULL
)

psvr_mape_linear(
  mode = "regression",
  engine = "psvr",
  cost = NULL,
  margin = NULL,
  sym_type = NULL
)

Arguments

mode

Only "regression" is supported.

engine

Only "psvr" is available.

cost

Regularization parameter C > 0. Use hardhat::tune() to optimize. Mapped to cost_psvr(), whose default range is [-2, 10] on the log2 scale (about 0.25 to 1024). That range is adequate here, where typical \(\epsilon\)-SVR optima lie in [10, 100]. The LS-SVR specs (psvr_rmspe_specs) map cost to \(\Gamma\) and need a much wider range; see there.

margin

Epsilon tube half-width \(\epsilon \ge 0\) expressed as a percentage of each target value. Use hardhat::tune() to optimize. Mapped to margin_percentage() with default range [1, 20] (percentage units). Named to match parsnip::svm_rbf(); note the units differ from dials::svm_margin(), which is absolute rather than percentage.

rbf_sigma

RBF bandwidth \(\sigma > 0\). Use hardhat::tune() to optimize. Mapped to rbf_sigma_psvr(), whose default range [-3, 1] on the log10 scale is a fixed, conservative fallback. The range does not finalize automatically from the training data. rbf_sigma_psvr() sets finalize = NULL, so dials::finalize() leaves it untouched. To centre the range on the data, pass rbf_sigma_psvr_data() computed on the preprocessed predictors explicitly — via update() on the extracted parameter set, or via psvr_option_add() for a workflow set. (RBF specs only.)

sym_type

Symmetry type: "none" (default) fits the non-symmetric \(\epsilon\)-SVR of Model 1; "even" (a = 1) and "odd" (a = -1) fit the symmetric \(\epsilon\)-SVR of Model 2. Use hardhat::tune() to optimise over the levels during CV; see sym_type_param() to restrict which levels are searched.

degree

Polynomial degree \(\ge 1\). Use hardhat::tune() to optimize. (Polynomial specs only.)

scale_factor

Polynomial constant term (coef0). Use hardhat::tune() to optimize. (Polynomial specs only.)

Value

A parsnip model_spec object of the corresponding class.

Examples

library(parsnip)
spec <- psvr_mape_rbf(cost = 10, margin = 1, rbf_sigma = 1) |>
  set_engine("psvr")

spec_poly <- psvr_mape_poly(cost = 10, margin = 1, degree = 2,
                            scale_factor = 1) |>
  set_engine("psvr")

spec_lin <- psvr_mape_linear(cost = 10, margin = 1) |>
  set_engine("psvr")

# Symmetric epsilon-SVR (Model 2) via the sym_type argument:
spec_sym <- psvr_mape_rbf(cost = 10, margin = 1, rbf_sigma = 1,
                          sym_type = "even") |>
  set_engine("psvr")