hai_auto_knn: Boilerplate Workflow

In healthyR.ai: The Machine Learning and AI Modeling Companion to 'healthyR'

Boilerplate Workflow

Description

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.

Details

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

Value

A list

Author(s)

Steven P. Sanderson II, MPH

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()

Examples

## Not run: 
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

## End(Not run)

healthyR.ai documentation built on June 8, 2025, 11:09 a.m.