Create an explicit governed tuning grid — create_gazepoint_tuning

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_tuning_grid> candidates=4
#>   candidate_id
#>  candidate_001
#>  candidate_002
#>  candidate_003
#>  candidate_004
#>                                                              label engine
#>   glm [engine:default; prep:center=TRUE,scale=TRUE; threshold=0.4]    glm
#>  glm [engine:default; prep:center=FALSE,scale=TRUE; threshold=0.4]    glm
#>   glm [engine:default; prep:center=TRUE,scale=TRUE; threshold=0.5]    glm
#>  glm [engine:default; prep:center=FALSE,scale=TRUE; threshold=0.5]    glm
#>  threshold complexity interpretability
#>        0.4        low             high
#>        0.4        low             high
#>        0.5        low             high
#>        0.5        low             high