---
title: "Workflow Stages"
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{Workflow Stages}
output:
  knitr:::html_vignette:
    toc: yes
---
  
```{r}
#| label: setup
#| include: false
knitr::opts_chunk$set(
  digits = 3,
  collapse = TRUE,
  comment = "#>"
)
options(digits = 3)
```

Workflows encompasses the three main stages of the modeling _process_: pre-processing of data, model fitting, and post-processing of results. This page enumerates the possible operations for each stage that have been implemented to date.

## Pre-processing

There are three options for pre-processing but you can only use one of them in a single workflow:

 * A standard [model formula](https://cran.r-project.org/doc/manuals/r-release/R-intro.html#Formulae-for-statistical-models) via `add_formula()`.
 
 * A tidyselect interface via `add_variables()` that [strictly preserves the class](https://www.tidyverse.org/blog/2020/09/workflows-0-2-0/) of your columns.
 
 * A recipe object via `add_recipe()`.

## Model Fitting

`parsnip` model specifications are the only option here, specified via `add_model()`.

When using a preprocessor, you may need an additional formula for special model terms (e.g. for mixed models or generalized linear models). In these cases, specify that formula using `add_model()`'s `formula` argument, which will be passed to the underlying model when `fit()` is called.

## Post-processing

`tailor` post-processors are the only option here, specified via `add_tailor()`. Some examples of post-processing model predictions could include adding a probability threshold for two-class problems, calibration of probability estimates, truncating the possible range of predictions, and so on.