create_gazepoint_tuning_grid: Create an explicit governed tuning grid

In gp3ml: Governance-First Predictive Modelling for 'Gazepoint' Research

Create an explicit governed tuning grid

Description

Candidate values are fully materialized before evaluation. No hidden metric, default ranking rule, or automatic winner is created.

Usage

create_gazepoint_tuning_grid(
  engine,
  engine_grid = list(),
  preprocessor_grid = list(),
  thresholds = 0.5,
  complexity = NA,
  interpretability = NA,
  labels = NULL
)

## S3 method for class 'gp3ml_tuning_grid'
print(x, ...)

Arguments

engine	One or more governed engine names.
engine_grid	Named list of engine-argument candidate values.
preprocessor_grid	Named list of preprocessing-argument candidate values.
thresholds	One or more explicit classification thresholds.
complexity	Optional complexity labels or numeric scores.
interpretability	Optional interpretability labels or numeric scores.
labels	Optional candidate labels.
x	An object returned by the corresponding gp3ml constructor, evaluator, summarizer, or validator.
...	Additional arguments passed to the print method.

Value

A gp3ml_tuning_grid with one row per explicit candidate.

Examples

grid <- create_gazepoint_tuning_grid(
  engine = "glm",
  preprocessor_grid = list(center = c(TRUE, FALSE), scale = TRUE),
  thresholds = c(0.4, 0.5),
  complexity = "low",
  interpretability = "high"
)
grid

gp3ml documentation built on Aug. 23, 2026, 5:11 p.m.