TMwR
Code and content for “Tidy Modeling with R”
TMwR is the source repository for “Tidy Modeling with R,” a book that demonstrates how to use tidyverse and tidymodels packages to build high-quality statistical and machine learning models.
The book provides a comprehensive guide to the tidymodels framework, showing how its consistent API and workflow tools solve common modeling challenges like data preprocessing, model specification, hyperparameter tuning, and performance evaluation. It teaches data scientists how to apply tidy data principles to the entire modeling process, from initial data exploration through final model deployment. The book emphasizes reproducible, production-ready modeling workflows that integrate cleanly with existing tidyverse data analysis pipelines.
Contributors#
Resources featuring TMwR#
How to train, evaluate, and deploy a machine learning workflow with tidymodels & Posit Team
Helpful resources: Github: https://github.com/simonpcouch/mutagen Follow-up Q&A Session: https://youtube.com/live/vwBVOBQfc_U If you want to book a call with our team to chat more about Posit products: pos.it/chat-with-us Don’t want to meet, but curious who else on your team is using Posit? pos.it/connect-us Blog post on tidymodels + Posit Connect: https://posit.co/blog/pharmaceutical-machine-learning-with-tidymodels-and-posit-connect/ Tidy Modeling with R book: https://www.tmwr.org/
Timestamps: 1:44 - Three steps for developing a machine learning model 3:35 - What is a machine learning model? 7:02 - Overview of machine learning with Posit Team 7:36: Step 1: Understand and clean data 11:05 - Step 2: Train and evaluate models (why you might be interested using tidymodels) 23:02 - Step 3: Deploying a machine learning model from Posit Workbench to Posit Connect 30:14 - Summary 31:21 - Helpful resources
Machine learning models are all around us, from Netflix movie recommendations to Zillow property value estimates to email spam filters.
As these models play an increasingly large role in our personal and professional lives, understanding and embracing them has never been more important; machine learning helps us make better, data-driven decisions.
The tidymodels framework is a powerful set of tools for building—and getting value out of—machine learning models with R.
Data scientists use tidymodels to:
- Gain access to a wide variety of machine learning methods
- Guard against common mistakes
- Easily deploy models through tidymodels’ integration with vetiver
Join Simon Couch from the tidyverse team on Wednesday, October 25th at 11am ET as he walks through an end-to-end machine learning workflow with Posit Team.
No registration is required to attend - simply add it to your calendar using this link: pos.it/team-demo
