EZtune: A Package for Automated Hyperparameter Tuning in R

03/03/2023
by   Jill Lundell, et al.
0

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.

READ FULL TEXT
research
03/13/2023

Tuning support vector machines and boosted trees using optimization algorithms

Statistical learning methods have been growing in popularity in recent y...
research
08/30/2021

To tune or not to tune? An Approach for Recommending Important Hyperparameters

Novel technologies in automated machine learning ease the complexity of ...
research
07/11/2017

Towards an automated method based on Iterated Local Search optimization for tuning the parameters of Support Vector Machines

We provide preliminary details and formulation of an optimization strate...
research
06/04/2019

A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers

For many machine learning algorithms, predictive performance is critical...
research
08/01/2020

Posterior Impropriety of some Sparse Bayesian Learning Models

Sparse Bayesian learning models are typically used for prediction in dat...
research
04/26/2019

A Novel Orthogonal Direction Mesh Adaptive Direct Search Approach for SVM Hyperparameter Tuning

In this paper, we propose the use of a black-box optimization method cal...
research
09/16/2019

Learning to Tune XGBoost with XGBoost

In this short paper we investigate whether meta-learning techniques can ...

Please sign up or login with your details

Forgot password? Click here to reset