View source: R/boilerplate-rangers.R
hai_auto_ranger | R Documentation |
This is a boilerplate function to create automatically the following:
recipe
model specification
workflow
tuned model (grid ect)
hai_auto_ranger(
.data,
.rec_obj,
.splits_obj = NULL,
.rsamp_obj = NULL,
.tune = TRUE,
.grid_size = 10,
.num_cores = 1,
.best_metric = "f_meas",
.model_type = "classification"
)
.data |
The data being passed to the function. The time-series object. |
.rec_obj |
This is the recipe object you want to use. You can use
|
.splits_obj |
NULL is the default, when NULL then one will be created. |
.rsamp_obj |
NULL is the default, when NULL then one will be created. It
will default to creating an |
.tune |
Default is TRUE, this will create a tuning grid and tuned workflow |
.grid_size |
Default is 10 |
.num_cores |
Default is 1 |
.best_metric |
Default is "f_meas". You can choose a metric depending on the
model_type used. If |
.model_type |
Default is |
This uses the parsnip::rand_forest()
with the engine
set to kernlab
A list
Steven P. Sanderson II, MPH
https://parsnip.tidymodels.org/reference/rand_forest.html
Other Boiler_Plate:
hai_auto_c50()
,
hai_auto_cubist()
,
hai_auto_earth()
,
hai_auto_glmnet()
,
hai_auto_knn()
,
hai_auto_svm_poly()
,
hai_auto_svm_rbf()
,
hai_auto_wflw_metrics()
,
hai_auto_xgboost()
Other Ranger:
hai_ranger_data_prepper()
## Not run:
data <- iris
rec_obj <- hai_ranger_data_prepper(data, Species ~ .)
auto_ranger <- hai_auto_ranger(
.data = data,
.rec_obj = rec_obj,
.best_metric = "f_meas"
)
auto_ranger$recipe_info
## End(Not run)
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