Tidy Tuning Tools

The ability to tune models is important. 'tune' contains functions and classes to be used in conjunction with other 'tidymodels' packages for finding reasonable values of hyper-parameters in models, preprocessing methods, and post-processing steps.

tune

Overview

The goal of tune is to facilitate hyperparameter tuning for the tidymodels packages. It relies heavily on recipes, parsnip, and dials.

Installation

Install from CRAN:

install.packages("tune", repos = "http://cran.r-project.org") #or your local mirror

or you can install the current development version using:

# install.packages("pak")
pak::pak("tidymodels/tune")

Examples

There are several package vignettes, as well as articles available at tidymodels.org, demonstrating how to use tune.

Good places to begin include:

• Getting started with cell segmentation data
• Getting started with Ames housing data

More advanced resources available are:

• Basic grid search for an SVM model
• Iterative Bayesian optimization of a classification model
• Advanced text mining example
• Notes on optimizations and parallel processing
• Details on acquisition function for scoring parameter combinations

Contributing

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

• For questions and discussions about tidymodels packages, modeling, and machine learning, please post on Posit Community.

• If you think you have encountered a bug, please submit an issue.

• Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.

• Check out further details on contributing guidelines for tidymodels packages and how to get help.