A workflow is an object that can bundle together your pre-processing, modeling, and post-processing requests. For example, if you have a
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
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.
You can install workflows from CRAN with:
You can install the development version from GitHub with:
# install.packages("devtools") devtools::install_github("tidymodels/workflows")
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:
and a model object:
To use these, you would generally run:
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:
Now you can prepare the recipe and estimate the model via a single call to
car_wflow_fit <- fit(car_wflow, data = mtcars)
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Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.