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https://github.com/tidymodels/tune
Tools for tidy parameter tuning
https://github.com/tidymodels/tune
Last synced: 2 days ago
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Tools for tidy parameter tuning
- Host: GitHub
- URL: https://github.com/tidymodels/tune
- Owner: tidymodels
- License: other
- Created: 2019-08-09T20:49:40.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2024-10-26T01:53:13.000Z (17 days ago)
- Last Synced: 2024-10-31T22:03:17.498Z (11 days ago)
- Language: R
- Homepage: https://tune.tidymodels.org
- Size: 131 MB
- Stars: 278
- Watchers: 15
- Forks: 42
- Open Issues: 71
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# tune 
[](https://github.com/tidymodels/tune/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/tidymodels/tune?branch=main)
[](https://lifecycle.r-lib.org/articles/stages.html)
[](https://CRAN.R-project.org/package=tune)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
## Overview
The goal of tune is to facilitate hyperparameter tuning for the tidymodels packages. It relies heavily on [recipes](https://recipes.tidymodels.org/), [parsnip](https://parsnip.tidymodels.org/), and [dials](https://dials.tidymodels.org/).
## Installation
Install from CRAN:
```r
install.packages("tune", repos = "http://cran.r-project.org") #or your local mirror
```
or you can install the current development version using:
```r
# install.packages("pak")
pak::pak("tidymodels/tune")
```
## Examples
There are several package vignettes, as well as articles available at [tidymodels.org](https://www.tidymodels.org/), demonstrating how to use tune.
Good places to begin include:
- [Getting started with cell segmentation data](https://www.tidymodels.org/start/tuning/)
- [Getting started with Ames housing data](https://tune.tidymodels.org/articles/getting_started.html)
More advanced resources available are:
- [Basic grid search for an SVM model](https://www.tidymodels.org/learn/work/tune-svm/)
- [Iterative Bayesian optimization of a classification model](https://www.tidymodels.org/learn/work/bayes-opt/)
- [Advanced text mining example](https://tune.tidymodels.org/articles/extras/text_analysis.html)
- [Notes on optimizations and parallel processing](https://tune.tidymodels.org/articles/extras/optimizations.html)
- [Details on acquisition function for scoring parameter combinations](https://tune.tidymodels.org/articles/acquisition_functions.html)
## Contributing
This project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). 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](https://community.rstudio.com/new-topic?category_id=15&tags=tidymodels,question).
- If you think you have encountered a bug, please [submit an issue](https://github.com/tidymodels/tune/issues).
- Either way, learn how to create and share a [reprex](https://reprex.tidyverse.org/articles/articles/learn-reprex.html) (a minimal, reproducible example), to clearly communicate about your code.
- Check out further details on [contributing guidelines for tidymodels packages](https://www.tidymodels.org/contribute/) and [how to get help](https://www.tidymodels.org/help/).