en_benchmark: Tunes a model via elastic net with 'glmnet' and returns

In jillbo1000/EZtuneTest:

Description

en_benchmark tunes a model with glmnet and returns a one row data.frame with the results. rsample is used to split the dataset into training and test datasets. The results returned

Usage

en_benchmark(x, y, name = "Data", loss = "default")

Arguments

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.
loss	Value for loss argument in glmnet. Options are "default", "class", "auc", "mse", or "mae". If "default" is selected,

Value

Saves a matrix to the indicated path that contains the results for each of the runs. The final file contains the following variables:

data	Name of the dataset.
package	This will always be "glmnet".
method	Type of model that was fit. This will always be "en" for elastic net.
optimizer	Optimization method. It will be "1se" or "min" to reflect the criteria used for lambda selection by glmnet.
assess	The method used to assess the model during tuning. It will always 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.

The models are verified using rsample to split the data into training and testing datasets.

See Also

tm_benchmark, EZtune::eztune

jillbo1000/EZtuneTest documentation built on Oct. 5, 2021, 4:16 p.m.