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Parsnip model specs: LS-SVR with RMSPE loss (Model 3)

psvr_rmspe_specs.Rd

Create parsnip model specifications for rmspe_lssvr() with a fixed kernel type. cost maps to the regularization parameter Γ.

Usage

psvr_rmspe_rbf(
  mode = "regression",
  engine = "psvr",
  cost = NULL,
  rbf_sigma = NULL
)

psvr_rmspe_poly(
  mode = "regression",
  engine = "psvr",
  cost = NULL,
  degree = NULL,
  scale_factor = NULL
)

psvr_rmspe_linear(mode = "regression", engine = "psvr", cost = NULL)

Arguments

mode

Only "regression" is supported.

engine

Only "psvr" is available.

cost

Regularization parameter Γ > 0. Use hardhat::tune() to optimize. Mapped to cost_psvr() with range [-2, 10] on the log2 scale — wider than dials::cost() to cover the larger values needed by LS-SVR models.

rbf_sigma

RBF bandwidth σ > 0. Use hardhat::tune() to optimize. Mapped to rbf_sigma_psvr(); the search range auto-finalizes using the median-distance heuristic when training data are available. (RBF specs only.)

degree

Polynomial degree ≥ 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.

Engine arguments

The precondition argument of rmspe_lssvr() is exposed as a non-tunable engine argument. Pass it via parsnip::set_engine(), e.g. set_engine("psvr", precondition = "always"). Default is "auto". See rmspe_lssvr() for accepted values and semantics.

Examples

if (FALSE) { # \dontrun{
library(parsnip)
spec <- psvr_rmspe_rbf(cost = 1000, rbf_sigma = 1) |>
  set_engine("psvr")

spec_poly <- psvr_rmspe_poly(cost = 1000, degree = 2, scale_factor = 1) |>
  set_engine("psvr")

spec_lin <- psvr_rmspe_linear(cost = 1000) |>
  set_engine("psvr")
} # }