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workflows

What is a workflow?

A workflow is an object that can bundle together your pre-processing, modeling, and post-processing requests. For example, if you have a recipe and parsnip model, these can be combined into a workflow. The advantages are:

• You don’t have to keep track of separate objects in your workspace.

• The recipe prepping and model fitting can be executed using a single call to fit().

• If you have custom tuning parameter settings, these can be defined using a simpler interface when combined with tune.

• In the future, workflows will be able to add post-processing operations, such as modifying the probability cutoff for two-class models.

Installation

You can install workflows from CRAN with:

install.packages("workflows")

You can install the development version from GitHub with:

# install.packages("pak")
pak::pak("tidymodels/workflows")

Example

Suppose you were modeling data on cars. Say…the fuel efficiency of 32 cars. You know that the relationship between engine displacement and miles-per-gallon is nonlinear, and you would like to model that as a spline before adding it to a Bayesian linear regression model. You might have a recipe to specify the spline:

library(recipes)
library(parsnip)
library(workflows)

spline_cars <- recipe(mpg ~ ., data = mtcars) %>% 
  step_ns(disp, deg_free = 10)

and a model object:

bayes_lm <- linear_reg() %>% 
  set_engine("stan")

To use these, you would generally run:

spline_cars_prepped <- prep(spline_cars, mtcars)
bayes_lm_fit <- fit(bayes_lm, mpg ~ ., data = juice(spline_cars_prepped))

You can’t predict on new samples using bayes_lm_fit without the prepped version of spline_cars around. You also might have other models and recipes in your workspace. This might lead to getting them mixed-up or forgetting to save the model/recipe pair that you are most interested in.

workflows makes this easier by combining these objects together:

car_wflow <- workflow() %>% 
  add_recipe(spline_cars) %>% 
  add_model(bayes_lm)

Now you can prepare the recipe and estimate the model via a single call to fit():

car_wflow_fit <- fit(car_wflow, data = mtcars)

You can alter existing workflows using update_recipe() / update_model() and remove_recipe() / remove_model().

Contributing

This project is released with a Contributor Code of Conduct. 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.

• If you think you have encountered a bug, please submit an issue.

• Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.

• Check out further details on contributing guidelines for tidymodels packages and how to get help.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.