EZtune: A Package for Automated Hyperparameter Tuning in R
This provides an automated tool for users, especially novices in statistical learning or R, to improve model performance, but it is incremental as it builds on existing tuning methods.
The paper tackles the problem of hyperparameter tuning for statistical learning models by introducing EZtune, an R package with a simple user interface that tunes models like support vector machines and gradient boosting machines, showing it can tune these models effectively.
Statistical learning models have been growing in popularity in recent years. Many of these models have hyperparameters that must be tuned for models to perform well. Tuning these parameters is not trivial. EZtune is an R package with a simple user interface that can tune support vector machines, adaboost, gradient boosting machines, and elastic net. We first provide a brief summary of the the models that EZtune can tune, including a discussion of each of their hyperparameters. We then compare the ease of using EZtune, caret, and tidymodels. This is followed with a comparison of the accuracy and computation times for models tuned with EZtune and tidymodels. We conclude with a demonstration of how how EZtune can be used to help select a final model with optimal predictive power. Our comparison shows that EZtune can tune support vector machines and gradient boosting machines with EZtune also provides a user interface that is easy to use for a novice to statistical learning models or R.