tm_benchmark: Tunes a model via tidymodels and returns benchmarking results
In jillbo1000/EZtuneTest:

tm_benchmark tunes a model with tidymodels and returns the accuracy results and computation time in seconds.

tm_benchmark(
  x,
  y,
  name = "Data",
  method = "svm",
  test = "grid",
  grid_size = 5,
  bayes_iter = 10
)

`x`	Matrix or data frame containing the dependent variables.
`y`	Vector of responses. Can either be a factor or a numeric vector.
`name`	Name of the dataset. Used to name the output file and as an identifier within the output dataset.
`method`	Model to be fit. Choices are "ada" for adaboost, "en" for elastic net, "gbm" for gradient boosting machines, and "svm" for support vector machines.
`test`	Type of tuning to be done. Choices are "bayes" for iterative Bayesian optimization and "grid" for grid search.
`grid_size`	Number of values for each hyperparameter for tidymodels grid. This is ignored if iterative Bayesian optimization is selected.
`bayes_iter`	Number of iterations for the Bayesian optimization. This is ignored if test = "grid"

Returns a single row matrix with the following elements:

`data`	Name of the dataset.
`package`	This will always be "tidymodels".
`method`	Type of model that was fit. It will gbm for gradient boosting machines or svm for support vector machines.
`optimizer`	Type of optimizer used. It will be Grid or Iterative Bayes.
`assess`	The method used to assess the model during tuning. For tidymodels it will be cross-validation.
`tuned_on`	Metric that was used as the loss function
`acc_rmse`	The best accuracy or RMSE obtained from the model as determined by a validation dataset.
`auc_mae`	The best AUC or MAE obtained from the model as determined by a validation dataset.
`time`	Time to complete the calculations in seconds.