Grid search with cross-validation (R parity) — morie_grid_search

Wraps caret::train with method = "glm" (classification) or "lm" (regression) by default; users can pass any caret method.

Usage

morie_grid_search_cv(
  x,
  y,
  method = NULL,
  tune_grid = NULL,
  cv = 5L,
  task = "auto",
  seed = 0L
)

Arguments

x: Numeric predictor matrix.
y: Response.
method: caret method id (default chosen by task).
tune_grid: data.frame of hyperparameter combos to evaluate.
cv: CV folds.
task: "auto", "classification", or "regression".
seed: RNG seed.

Value

Named list: estimate (best CV score), best_params, best_score, cv_results_params, cv_results_mean_score, task, n, method.

Examples

morie_grid_search_cv(
  x = matrix(rnorm(150), 50, 3), y = rnorm(50),
  method = "lm", tune_grid = data.frame(intercept = c(TRUE, FALSE)),
  cv = 3L, task = "regression", seed = 1L
)
#> Loading required package: ggplot2
#> Loading required package: lattice
#> 
#> Attaching package: ‘caret’
#> The following object is masked from ‘package:future’:
#> 
#>     cluster
#> $estimate
#> [1] 0.9653427
#> 
#> $best_params
#> $best_params$intercept
#> [1] TRUE
#> 
#> 
#> $best_score
#> [1] 0.9653427
#> 
#> $cv_results_params
#>   intercept    RMSESD RsquaredSD     MAESD
#> 1     FALSE 0.2194193 0.06324230 0.1308791
#> 2      TRUE 0.2183549 0.05663636 0.1300713
#> 
#> $cv_results_mean_score
#> [1] 0.9653427 0.9517002
#> 
#> $task
#> [1] "regression"
#> 
#> $n
#> [1] 50
#> 
#> $method
#> [1] "Grid search CV (lm)"
#>