load_opt_data: Reads in data files from the optimization tests for EZtune
In jillbo1000/EZtuneTest:

Description Usage Arguments Value See Also

load_opt_data reads all of the csv files in a folder that contain the output from the optimization tests.

1	load_opt_data(path, ...)

path

This is the path to the directory that contains the optimization output files.

Returns a dataset that has all of the outputs from the optimization tests recorded in the files in a folder. Typically, a folder will contain the tests for one dataset. The dataset contains the following variables:

`data`	Name of the dataset.
`method`	Type of model that was fit. It will an abbreviation for adaboost, elastic net, gradient boosting machines, or support vector machines.
`optimizer`	Type of optimizer used. It will either be ga for a genetic algorithm or hjn for a Hookes-Jeeves algorithm.
`fast`	The argument passed to the fast option. If it is a 1, a value of TRUE was passed and if it was 0 a value of FALSE was passed.
`cross`	n for n-fold cross-validation in the optimization. It was only used if fast was FALSE.
`loss_type`	\|ar_ref - 0.5\|
`seed`	-log10(av_stat), where 0 is replaced with 0.001 to avoid taking the log of 0.
`loss`	Type of loss used as an optimizer. If the dataset has a continuous response, the options are mse for mean squared error and mae for mean absolute error. If the response is binary, the options are acc for accuracy and auc for area under the ROC curve.
`loss_mse_acc_10`	Estimate of the accuracy or mean squared error as computed using the eztune_cv function with 10 fold cross validation.
`loss_mae_auc_10`	Estimate of the area under the curve or mean absolute error as computed using the eztune_cv function with 10 fold cross validation.