tidymodels

Convert statistical analysis objects from R into tidy format

Easily install and load the tidymodels packages

An R package for tidyverse-friendly statistical inference

A tidy unified interface to models

Code and content for "Tidy Modeling with R"

Pipeable steps for feature engineering and data preprocessing to prepare for modeling

Explore correlations in R

Local Interpretable Model-Agnostic Explanations (R port of original Python package)

Tidy methods for measuring model performance

Tools for tidy parameter tuning

Classes and functions to create and summarize resampling objects

An R package for tidy stacked ensemble modeling

Run predictions inside the database

Modeling Workflows

Extra recipes for Text Processing

Extra recipes for predictor embeddings

Extra recipes steps for dealing with unbalanced data

Reduce the size of model objects saved to disk

Parsnip wrappers for survival models

Tools for post-processing class probability estimates

A tidy unified interface to clustering models

Tools for creating tuning parameter values

Construct Modeling Packages

Bayesian comparisons of models using resampled statistics

Website and materials for tidymodels workshops

The most recent version of the Applied Machine Learning notes

Legacy Source of tidymodels.org

Create a collection of modeling workflows

Boilerplate Code for tidymodels

Create and summarize spatial resampling objects 🗺

Run models inside a database using R

Parsnip wrappers for mixed-level and hierarchical models

High-Level Modeling Functions with 'torch'

Learn tidymodels with interactive learnr primers

Additional functions for model tuning

parsnip wrappers for tree-based models

Turn Tidymodels Workflows Into Series of Equations