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Declare a computational engine and specific arguments — set_engine • parsnip

set_engine() is used to specify which package or system will be used to fit the model, along with any arguments specific to that software.

set_engine(object, engine, ...)

object

A model specification.

engine

A character string for the software that should be used to fit the model. This is highly dependent on the type of model (e.g. linear regression, random forest, etc.).

...

Any optional arguments associated with the chosen computational engine. These are captured as quosures and can be tuned with tune().

An updated model specification.

In parsnip,

• the model type differentiates basic modeling approaches, such as random forests, logistic regression, linear support vector machines, etc.,

• the mode denotes in what kind of modeling context it will be used (most commonly, classification or regression), and

• the computational engine indicates how the model is fit, such as with a specific R package implementation or even methods outside of R like Keras or Stan.

Use show_engines() to get a list of possible engines for the model of interest.

Modeling functions in parsnip separate model arguments into two categories:

• Main arguments are more commonly used and tend to be available across engines. These names are standardized to work with different engines in a consistent way, so you can use the parsnip main argument trees, instead of the heterogeneous arguments for this parameter from ranger and randomForest packages (num.trees and ntree, respectively). Set these in your model type function, like rand_forest(trees = 2000).

• Engine arguments are either specific to a particular engine or used more rarely; there is no change for these argument names from the underlying engine. The ... argument of set_engine() allows any engine-specific argument to be passed directly to the engine fitting function, like set_engine("ranger", importance = "permutation").

# First, set main arguments using the standardized names
logistic_reg(penalty = 0.01, mixture = 1/3) |>
  # Now specify how you want to fit the model with another argument
  set_engine("glmnet", nlambda = 10) |>
  translate()
#> Logistic Regression Model Specification (classification)
#> 
#> Main Arguments:
#>   penalty = 0.01
#>   mixture = 1/3
#> 
#> Engine-Specific Arguments:
#>   nlambda = 10
#> 
#> Computational engine: glmnet 
#> 
#> Model fit template:
#> glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
#>     alpha = 1/3, nlambda = 10, family = "binomial")

# Many models have possible engine-specific arguments
decision_tree(tree_depth = 5) |>
  set_engine("rpart", parms = list(prior = c(.65,.35))) |>
  set_mode("classification") |>
  translate()
#> Decision Tree Model Specification (classification)
#> 
#> Main Arguments:
#>   tree_depth = 5
#> 
#> Engine-Specific Arguments:
#>   parms = list(prior = c(0.65, 0.35))
#> 
#> Computational engine: rpart 
#> 
#> Model fit template:
#> rpart::rpart(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
#>     maxdepth = 5, parms = list(prior = c(0.65, 0.35)))

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