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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# parsnip <a href="https://parsnip.tidymodels.org/"><img src="man/figures/logo.png" align="right" height="138" alt="a drawing of a parsnip on a beige background" /></a>
<!-- badges: start -->
[](https://github.com/tidymodels/parsnip/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/tidymodels/parsnip?branch=main)
[](https://CRAN.R-project.org/package=parsnip)
[](https://CRAN.R-project.org/package=parsnip)
[](https://lifecycle.r-lib.org/articles/stages.html)
<!-- badges: end -->
## Introduction
The goal of parsnip is to provide a tidy, unified interface to models that can be used to try a range of models without getting bogged down in the syntactical minutiae of the underlying packages.
## Installation
```{r, eval = FALSE}
# The easiest way to get parsnip is to install all of tidymodels:
install.packages("tidymodels")
# Alternatively, install just parsnip:
install.packages("parsnip")
# Or the development version from GitHub:
# install.packages("pak")
pak::pak("tidymodels/parsnip")
```
## Getting started
One challenge with different modeling functions available in R _that do the same thing_ is that they can have different interfaces and arguments. For example, to fit a random forest regression model, we might have:
```{r eval = FALSE}
# From randomForest
rf_1 <- randomForest(
y ~ .,
data = dat,
mtry = 10,
ntree = 2000,
importance = TRUE
)
# From ranger
rf_2 <- ranger(
y ~ .,
data = dat,
mtry = 10,
num.trees = 2000,
importance = "impurity"
)
# From sparklyr
rf_3 <- ml_random_forest(
dat,
intercept = FALSE,
response = "y",
features = names(dat)[names(dat) != "y"],
col.sample.rate = 10,
num.trees = 2000
)
```
Note that the model syntax can be very different and that the argument names (and formats) are also different. This is a pain if you switch between implementations.
In this example:
* the **type** of model is "random forest",
* the **mode** of the model is "regression" (as opposed to classification, etc), and
* the computational **engine** is the name of the R package.
The goals of parsnip are to:
* Separate the definition of a model from its evaluation.
* Decouple the model specification from the implementation (whether the implementation is in R, spark, or something else). For example, the user would call `rand_forest` instead of `ranger::ranger` or other specific packages.
* Harmonize argument names (e.g. `n.trees`, `ntrees`, `trees`) so that users only need to remember a single name. This will help _across_ model types too so that `trees` will be the same argument across random forest as well as boosting or bagging.
Using the example above, the parsnip approach would be:
```{r}
library(parsnip)
rand_forest(mtry = 10, trees = 2000) %>%
set_engine("ranger", importance = "impurity") %>%
set_mode("regression")
```
The engine can be easily changed. To use Spark, the change is straightforward:
```{r}
rand_forest(mtry = 10, trees = 2000) %>%
set_engine("spark") %>%
set_mode("regression")
```
Either one of these model specifications can be fit in the same way:
```{r}
set.seed(192)
rand_forest(mtry = 10, trees = 2000) %>%
set_engine("ranger", importance = "impurity") %>%
set_mode("regression") %>%
fit(mpg ~ ., data = mtcars)
```
A list of all parsnip models across different CRAN packages can be found at https://www.tidymodels.org/find/parsnip.
## Contributing
This project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). 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 RStudio Community](https://forum.posit.co/new-topic?category_id=15&tags=tidymodels,question).
- If you think you have encountered a bug, please [submit an issue](https://github.com/tidymodels/parsnip/issues).
- Either way, learn how to create and share a [reprex](https://reprex.tidyverse.org/articles/articles/learn-reprex.html) (a minimal, reproducible example), to clearly communicate about your code.
- Check out further details on [contributing guidelines for tidymodels packages](https://www.tidymodels.org/contribute/) and [how to get help](https://www.tidymodels.org/help/).