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.

```
# 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")
```

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:

```
# 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:

```
library(parsnip)
rand_forest(mtry = 10, trees = 2000) %>%
set_engine("ranger", importance = "impurity") %>%
set_mode("regression")
#> Random Forest Model Specification (regression)
#>
#> Main Arguments:
#> mtry = 10
#> trees = 2000
#>
#> Engine-Specific Arguments:
#> importance = impurity
#>
#> Computational engine: ranger
```

The engine can be easily changed. To use Spark, the change is straightforward:

```
rand_forest(mtry = 10, trees = 2000) %>%
set_engine("spark") %>%
set_mode("regression")
#> Random Forest Model Specification (regression)
#>
#> Main Arguments:
#> mtry = 10
#> trees = 2000
#>
#> Computational engine: spark
```

Either one of these model specifications can be fit in the same way:

```
set.seed(192)
rand_forest(mtry = 10, trees = 2000) %>%
set_engine("ranger", importance = "impurity") %>%
set_mode("regression") %>%
fit(mpg ~ ., data = mtcars)
#> parsnip model object
#>
#> Ranger result
#>
#> Call:
#> ranger::ranger(x = maybe_data_frame(x), y = y, mtry = min_cols(~10, x), num.trees = ~2000, importance = ~"impurity", num.threads = 1, verbose = FALSE, seed = sample.int(10^5, 1))
#>
#> Type: Regression
#> Number of trees: 2000
#> Sample size: 32
#> Number of independent variables: 10
#> Mtry: 10
#> Target node size: 5
#> Variable importance mode: impurity
#> Splitrule: variance
#> OOB prediction error (MSE): 5.976917
#> R squared (OOB): 0.8354559
```

A list of all parsnip models across different CRAN packages can be found at https://www.tidymodels.org/find/parsnip.

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For questions and discussions about tidymodels packages, modeling, and machine learning, please post on RStudio Community.

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