+ "markdown": "---\nformat:\n html:\n toc: false\n---\n\n\n\n## Welcome\n\nThese are the materials for workshops on [tidymodels](https://www.tidymodels.org/) offered at [posit::conf(2025)](https://posit.co/conference/). The tidymodels framework is a collection of packages for modeling and machine learning using [tidyverse](https://www.tidyverse.org/) principles. This website hosts the materials for both the _Introduction to Machine Learning in R with tidymodels_ and _Getting More Out of Feature Engineering and Tuning for Machine Learning_ courses.\n\n_Introduction to Machine Learning in R with tidymodels_ will teach you core tidymodels packages and their uses: data splitting/resampling with rsample, model fitting with parsnip, measuring model performance with yardstick, and model optimization using the tune package. Time permitting, you'll be introduced to basic pre-processing with recipes. You'll learn tidymodels syntax as well as the process of predictive modeling for tabular data.\n\n_Getting More Out of Feature Engineering and Tuning for Machine Learning_ will teach you about model optimization using the tune and finetune packages, including racing and iterative methods. You'll be able to do more sophisticated feature engineering with recipes. Time permitting, model ensembles via stacking will be introduced. This course is focused on the analysis of tabular data and does not include deep learning methods.\n\n## Is this workshop for me? <img src=\"slides/images/parsnip-flagger.jpg\" align=\"right\" height=\"150\"/>\n\nDepending on your background, one of _Introduction to Machine Learning in R with tidymodels_ or _Getting More Out of Feature Engineering and Tuning for Machine Learning_ might serve you better than the other.\n\n### Introduction to Machine Learning in R with tidymodels\n\nThis workshop is for you if you:\n\n- are comfortable using tidyverse packages to read data into R, transform and reshape data, and make a variety of graphs, and\n- have had some exposure to basic statistical concepts such as linear models, residuals, etc.\n\nIntermediate or expert familiarity with modeling or machine learning is not required. Interested students who have intermediate or expert familiarity with modeling or machine learning may be interested in the _Getting More Out of Feature Engineering and Tuning for Machine Learning_ workshop.\n\n### Getting More Out of Feature Engineering and Tuning for Machine Learning\n\nThis workshop is for you if you:\n\n- have the prerequisite skills listed for the _Introduction to Machine Learning in R with tidymodels_ workshops, \n- have used tidymodels packages like recipes, rsample, and parsnip, and\n- have some experience with evaluating statistical models using resampling techniques like v-fold cross-validation or the bootstrap.\n\nParticipants who are new to tidymodels or machine learning will benefit from taking the _Introduction to Machine Learning in R with tidymodels_ workshop before joining this one. Participants who have completed the \"Introduction to Machine Learning in R with tidymodels\" workshop previously will be well-prepared for this course.\n\n## Preparation\n\nThe process to set up your computer for either workshop will look the same. Please join the workshop with a computer that has the following installed (all available for free):\n\n- A recent version of R, available at <https://cran.r-project.org/>\n- A recent version of an IDE, either\n - RStudio Desktop (RStudio Desktop Open Source License, at least v2024.04.0), available at <https://posit.co/download/rstudio-desktop/> or \n - Positron (Desktop version, at least 2025.08.0-130), available at <https://positron.posit.co/download.html>\n- For _all of the slides_, the following R packages can be installed from the R console:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Install the packages for the workshop\npkgs <- \n c(\"almanac\", \"betacal\", \"bonsai\", \"brulee\", \"C50\", \"Cubist\", \"desirability2\", \n \"dimRed\", \"embed\", \"extrasteps\", \"finetune\", \"forested\", \n \"igraph\", \"important\", \"irlba\", \"kknn\", \"lightgbm\", \"lme4\", \"mirai\", \n \"parallelly\", \"plumber\", \"probably\", \"ranger\", \"RANN\", \"rpart\", \n \"rpart.plot\", \"RSpectra\", \"rules\", \"splines2\", \"stacks\", \"text2vec\", \n \"textrecipes\", \"tidymodels\", \"uwot\", \"vetiver\")\n\ninstall.packages(pkgs)\n```\n:::\n\n\n\nAlso, you should make sure that you have installed the newest version of a few packages. To check this, you can run the following code snippet. It will also prompt you to install the required versions if they are not already installed.\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrlang::check_installed(\"tidymodels\", version = \"1.4.1\")\nrlang::check_installed(\"embed\", version = \"1.2.0\")\n```\n:::\n\n\n\nIf you're a Windows user and encounter an error message during installation noting a missing Rtools installation, install Rtools using the installer linked [here](https://cran.r-project.org/bin/windows/Rtools/).\n\n## Slides\n\nThese slides are designed to use with live teaching and are published for workshop participants' convenience. They are not meant as standalone learning materials. For that, we recommend [tidymodels.org](https://www.tidymodels.org/start/) and [*Tidy Modeling with R*](https://www.tmwr.org/).\n\n### Introduction to Machine Learning in R with tidymodels\n\n- 01: [Introduction](slides/intro-01-introduction.html){target=\"_blank\"}\n- 02: [Your data budget](slides/intro-02-data-budget.html){target=\"_blank\"}\n- 03: [What makes a model?](slides/intro-03-what-makes-a-model.html){target=\"_blank\"}\n- 04: [Evaluating models](slides/intro-04-evaluating-models.html){target=\"_blank\"}\n- 05: [Tuning models](slides/intro-05-tuning-models.html){target=\"_blank\"}\n- 06: [Wrapping up](slides/intro-06-wrapping-up.html){target=\"_blank\"}\n\n### Getting More Out of Feature Engineering and Tuning for Machine Learning\n\n- 01: [Introduction](slides/advanced-01-introduction.html){target=\"_blank\"}\n- 02: [Model optimization by tuning](slides/advanced-02-model-tuning.html){target=\"_blank\"}\n- 03: [Grid search via racing](slides/advanced-03-racing.html){target=\"_blank\"}\n- 04: [Feature engineering: dummies and embeddings](slides/advanced-04-feature-engineering-part-one.html){target=\"_blank\"}\n- 05: [Feature engineering: splines, target encoding and dates](slides/advanced-05-feature-engineering-part-two.html){target=\"_blank\"}\n- 06: [Postprocessing](slides/advanced-06-postprocessing.html){target=\"_blank\"}\n- 07: [Feature selection](slides/advanced-07-feature-selection.html){target=\"_blank\"}\n- 08: [Wrapping up](slides/advanced-08-wrapping-up.html){target=\"_blank\"} \n\n### Extra content (time permitting)\n\n- [Intro: Using workflowsets](slides/intro-extra-workflowsets.html){target=\"_blank\"}\n- [Intro: Using recipes](slides/intro-extra-recipes.html){target=\"_blank\"}\n- [Advanced: Transit case study (includes stacking)](slides/extras-transit-case-study.html){target=\"_blank\"}\n- [Advanced: Iterative search](slides/extras-iterative-search.html){target=\"_blank\"}\n\nThere's also a page for [slide annotations](slides/annotations.html){target=\"_blank\"}; these are extra notes for selected slides.\n\n## Code\n\nQuarto files for working along [are available on GitHub](https://github.com/tidymodels/workshops/tree/main/classwork). (Don't worry if you haven't used Quarto before; it will feel familiar to R Markdown users.)\n\n## Past workshops\n\n### English\n\n- [August 2025](archive/2025-08-nyr/index.html) at New York Data Science & AI Conference\n- [August 2024](archive/2024-08-posit-conf/index.html) at posit::conf()\n- [September 2023](archive/2023-09-posit-conf/) at posit::conf()\n- [July 2023](archive/2023-07-nyr/) at the New York R Conference\n- [August 2022](archive/2022-08-Reykjavik-City/) in Reykjavik\n- [July 2022](archive/2022-07-RStudio-conf/index.html) at [rstudio::conf()](https://posit.co/blog/talks-and-workshops-from-rstudio-conf-2022/)\n\n### Spanish\n\n- [March 2024](archive/2024-03-conectaR-spanish/) at [conectaR](https://congresos.ucr.ac.cr/event/7/)\n\n## Acknowledgments {.appendix}\n\nThis website, including the slides, is made with [Quarto](https://quarto.org/). Please [submit an issue](https://github.com/tidymodels/workshops/issues) on the GitHub repo for this workshop if you find something that could be fixed or improved.\n\n## Reuse and licensing {.appendix}\n\nUnless otherwise noted (i.e. not an original creation and reused from another source), these educational materials are licensed under Creative Commons Attribution [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).\n",
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