TunedInput: Tuned Model Inputs

In MachineShop: Machine Learning Models and Tools

Tuned Model Inputs

Description

Recipe tuning over a grid of parameter values.

Usage

TunedInput(object, ...)

## S3 method for class 'recipe'
TunedInput(
  object,
  grid = expand_steps(),
  control = MachineShop::settings("control"),
  metrics = NULL,
  cutoff = MachineShop::settings("cutoff"),
  stat = MachineShop::settings("stat.TrainingParams"),
  ...
)

Arguments

object	untrained recipe.
...	arguments passed to other methods.
grid	RecipeGrid containing parameter values at which to evaluate a recipe, such as those returned by expand_steps.
control	control function, function name, or object defining the resampling method to be employed.
metrics	metric function, function name, or vector of these with which to calculate performance. If not specified, default metrics defined in the performance functions are used. Recipe selection is based on the first calculated metric.
cutoff	argument passed to the metrics functions.
stat	function or character string naming a function to compute a summary statistic on resampled metric values for recipe tuning.

Value

TunedModelRecipe class object that inherits from TunedInput and recipe.

See Also

fit, resample, set_optim

Examples

library(recipes)
data(Boston, package = "MASS")

rec <- recipe(medv ~ ., data = Boston) %>%
  step_pca(all_numeric_predictors(), id = "pca")

grid <- expand_steps(
  pca = list(num_comp = 1:2)
)

fit(TunedInput(rec, grid = grid), model = GLMModel)

MachineShop documentation built on Sept. 18, 2023, 5:06 p.m.