Find the best version of each algorithm based on the hyperparameters and 10-fold cross-validation.

Find the best version of each algorithm based on the hyperparameters and 10-fold cross-validation.

find_best_model(
  lasso_wf,
  rand_wf,
  xg_wf,
  class_folds,
  lasso_grid,
  rand_grid,
  xg_grid,
  metric_choice = "accuracy"
)

Arguments

lasso_wf

A lasso workflow (including the search space for the model)

rand_wf

A random forest workflow (including the search space for the model)

xg_wf

An XGBoost workflow (including the search space for the model)

class_folds

10-fold cross-validation samples

lasso_grid

The search spaces for lasso

rand_grid

The search spaces for random forest

xg_grid

The search space for XGBoost

metric_choice

The selected metrics for the model evaluation among accuracy, balanced accuracy (bal_accuracy), F-score (f_means), and Area under the ROC curve (roc_auc). The default value is accuracy.

Value

A list output that contains the best model output for lasso, random forest, and XGBoost.