Boilerplate Workflow

This is a boilerplate function to create automatically the following:

• recipe

• model specification

• workflow

• tuned model (grid ect)

Usage

hai_auto_knn(
  .data,
  .rec_obj,
  .splits_obj = NULL,
  .rsamp_obj = NULL,
  .tune = TRUE,
  .grid_size = 10,
  .num_cores = 1,
  .best_metric = "rmse",
  .model_type = "regression"
)

Arguments

.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 hai_knn_data_prepper() an automatic recipe_object.

.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 rsample::mc_cv() object.

.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 "rmse". You can choose a metric depending on the model_type used. If regression then see hai_default_regression_metric_set(), if classification then see hai_default_classification_metric_set().

.model_type

Default is regression, can also be classification.

Value

A list

Details

This uses the parsnip::nearest_neighbor() with the engine set to kknn

See also

Other Boiler_Plate: hai_auto_c50(), hai_auto_cubist(), hai_auto_earth(), hai_auto_glmnet(), hai_auto_ranger(), hai_auto_svm_poly(), hai_auto_svm_rbf(), hai_auto_wflw_metrics(), hai_auto_xgboost()

Author

Steven P. Sanderson II, MPH

Examples

if (FALSE) { # \dontrun{
library(dplyr)

data <- iris

rec_obj <- hai_knn_data_prepper(data, Species ~ .)

auto_knn <- hai_auto_knn(
  .data = data,
  .rec_obj = rec_obj,
  .best_metric = "f_meas",
  .model_type = "classification"
)

auto_knn$recipe_info
} # }