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, ...)
A model specification.
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
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.
show_engines() to get a list of possible engines for the model of
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
instead of the heterogeneous arguments for this parameter from ranger
and randomForest packages (
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
... argument of
set_engine() allows any engine-specific
argument to be passed directly to the engine fitting function, like
set_engine("ranger", importance = "permutation").
An updated model specification.
# 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() # 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()
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