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("devtools") devtools::install_github("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 = ., 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:

rand_forest(mtry = 10, trees = 2000) %>% set_engine("ranger", importance = "impurity") %>% set_mode("regression") %>% fit(mpg ~ ., data = mtcars) #> parsnip model object #> #> Fit time: 71ms #> Ranger result #> #> Call: #> ranger::ranger(formula = mpg ~ ., data = data, mtry = ~10, 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): 5.699772 #> R squared (OOB): 0.8430857

A list of all `parsnip`

models across different CRAN packages can be found at `tidymodels.org`

.

Data sets previously found in `parsnip`

are now find in the `modeldata`

package.

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