Tuned Model Inputs
Recipe tuning over a grid of parameter values.
TunedInput(x, ...)
## S3 method for class 'recipe'
TunedInput(
x,
grid = expand_steps(),
control = MachineShop::settings("control"),
metrics = NULL,
stat = MachineShop::settings("stat.train"),
cutoff = MachineShop::settings("cutoff"),
...
)x |
untrained |
... |
arguments passed to other methods. |
grid |
|
control |
control function, function name, or call 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. |
stat |
function or character string naming a function to compute a summary statistic on resampled metric values for recipe tuning. |
cutoff |
argument passed to the |
TunedModelRecipe class object that inherits from
TunedInput and recipe.
library(recipes) data(Boston, package = "MASS") rec <- recipe(medv ~ ., data = Boston) %>% step_pca(all_numeric(), -all_outcomes(), id = "pca") grid <- expand_steps( pca = list(num_comp = 1:2) ) fit(TunedInput(rec, grid = grid), model = GLMModel)
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