setup_SPLS: Setup SPLS Hyperparameters

In rtemis: Schema-Driven Machine Learning and Visualization

Setup SPLS Hyperparameters

Description

Setup hyperparameters for Sparse Partial Least Squares training.

Usage

setup_SPLS(
  k = 2L,
  eta = 0.5,
  kappa = 0.5,
  select = "pls2",
  fit = "simpls",
  classifier = "lda",
  scale_x = TRUE,
  scale_y = FALSE,
  eps = 1e-04,
  maxstep = 100L,
  ifw = FALSE
)

Arguments

k	(Tunable) Integer [1, Inf): Number of latent components.
eta	(Tunable) Numeric [0, 1): Sparsity threshold. Higher values select fewer features.
kappa	(Tunable) Numeric [0, 0.5]: Concavity of the surrogate direction vector problem.
select	Character {"pls2", "simpls"}: Feature selection algorithm (regression only).
fit	Character {"kernelpls", "widekernelpls", "simpls", "oscorespls"}: PLS algorithm used for model fitting (regression only).
classifier	Character {"lda", "logistic"}: Classifier fit on the latent components (classification only).
scale_x	Logical: If TRUE, scale features to unit variance.
scale_y	Logical: If TRUE, scale the outcome to unit variance (regression only).
eps	Numeric (0, Inf): Convergence tolerance (regression only).
maxstep	Integer [1, Inf): Maximum number of iterations per component (regression only).
ifw	(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in classification.

Details

Regression is fit with spls::spls and classification with spls::splsda, chosen from the outcome type. Parameters marked "regression only" or "classification only" are passed to the backend that accepts them and ignored by the other.

spls provides no case weights, so ifw cannot be honored: enabling it makes training abort.

Value

SPLSHyperparameters object.

Author(s)

EDG

Examples

spls_hyperparams <- setup_SPLS(k = 3L, eta = 0.7)
spls_hyperparams

rtemis documentation built on Oct. 20, 2026, 1:06 a.m.