# Tidymodels and machine learning

The {tidymodels} concept (Kuhn and Silge 2022) is a group of packages in support of modeling and machine learning. In the last section we learned how to manipulate a basic linear model though a combination of the base-R `lm()`

function and the tidyverse {broom} package along with the `nest()`

function. However, modeling can be much more involved. This basic overview introduces tidymodels, a conceptual approach to integrating tidyverse principles with modeling, machine learning, feature selection and tuning.

Beyond the core of integrating machine learning and modeling with the tidyverse, tidymodels supports a variety of useful analytical and computational approaches. A short list of examples includes **statistical analysis** (e.g. bootstrapping, hypothesis testing, k-means clustering, logistic regression, etc.), **robust modeling** (e.g. classification, least squares, resampling), creating **performance metrics**, **tuning, clustering, classification, text analysis, neural networks**, and more.

## Get started

Modelers and ML coders can approach tidymodels by

Engaging with the five-step tutorial (build a model, use recipes to pre-process data, evaluate with resampling, tune, and predict.

Dive deeper to find articles that help apply the tidymodels approach to your needs.

## References

*Tidy Modeling with r : A Framework for Modeling in the Tidyverse*. Sebastopol, CA : O’Reilly Media, 2022.