This package provides functions and methods to create and manipulate functions commonly used during modeling (e.g. fitting the model, making predictions, etc). It allows the user to manipulate how the same type of model can be created from different sources.
Motivation
Modeling functions across different R packages can have very different interfaces. If you would like to try different approaches, there is a lot of syntactical minutiae to remember. The problem worsens when you move inbetween platforms (e.g. doing a logistic regression in R’s glm
versus Spark’s implementation).
parsnip
tries to solve this by providing similar interfaces to models. For example, if you are fitting a random forest model and would like to adjust the number of trees in the forest there are different argument names to remember:

randomForest::randomForest
usesntree
, 
ranger::ranger
usesnum.trees
,
 Spark’s
sparklyr::ml_random_forest
usesnum_trees
.
Rather than remembering these values, a common interface to these models can be used with
library(parsnip)
rf_mod < rand_forest(trees = 2000)
The package makes the translation between trees
and the real names in each of the implementations.
Some terminology:
 The model type differentiates models. Example types are: random forests, logistic regression, linear support vector machines, etc.
 The mode of the model denotes how it will be used. Two common modes are classification and regression. Others would include “censored regression” and “risk regression” (parametric and Cox PH models for censored data, respectively), as well as unsupervised models (e.g. “clustering”).
 The computational engine indicates how the actual model might be fit. These are often R packages (such as
randomForest
orranger
) but might also be methods outside of R (e.g. Stan, Spark, and others).
parsnip
, similar to ggplot2
, dplyr
and recipes
, separates the specification of what you want to do from the actual doing. This allows us to create broader functionality for modeling.
Placeholders for Parameters
There are times where you would like to change a parameter from its default but you are not sure what the final value will be. This is the basis for model tuning where we use the tune package. Since the model is not executing when created, these types of parameters can be changed using the tune()
function. This provides a simple placeholder for the value.
tune_mtry < rand_forest(trees = 2000, mtry = tune())
tune_mtry
#> Random Forest Model Specification (unknown)
#>
#> Main Arguments:
#> mtry = tune()
#> trees = 2000
#>
#> Computational engine: ranger
This will come in handy later when we fit the model over different values of mtry
.
Specifying Arguments
Commonly used arguments to the modeling functions have their parameters exposed in the function. For example, rand_forest
has arguments for:

mtry
: The number of predictors that will be randomly sampled at each split when creating the tree models. 
trees
: The number of trees contained in the ensemble. 
min_n
: The minimum number of data points in a node that are required for the node to be split further.
The arguments to the default function are:
args(rand_forest)
#> function (mode = "unknown", engine = "ranger", mtry = NULL, trees = NULL,
#> min_n = NULL)
#> NULL
However, there might be other arguments that you would like to change or allow to vary. These are accessible using set_engine
. For example, ranger
has an option to set the internal random number seed. To set this to a specific value:
rf_with_seed <
rand_forest(trees = 2000, mtry = tune(), mode = "regression") %>%
set_engine("ranger", seed = 63233)
rf_with_seed
#> Random Forest Model Specification (regression)
#>
#> Main Arguments:
#> mtry = tune()
#> trees = 2000
#>
#> EngineSpecific Arguments:
#> seed = 63233
#>
#> Computational engine: ranger
Process
To fit the model, you must:
 have a defined model, including the mode,
 have no
tune()
parameters, and  specify a computational engine.
For example, rf_with_seed
above is not ready for fitting due the tune()
parameter. We can set that parameter’s value and then create the model fit:
#> parsnip model object
#>
#> Ranger result
#>
#> Call:
#> ranger::ranger(x = maybe_data_frame(x), y = y, mtry = min_cols(~4, x), num.trees = ~2000, 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: 4
#> Target node size: 5
#> Variable importance mode: none
#> Splitrule: variance
#> OOB prediction error (MSE): 5.57
#> R squared (OOB): 0.847
Or, using the randomForest
package:
set.seed(56982)
rf_with_seed %>%
set_args(mtry = 4) %>%
set_engine("randomForest") %>%
fit(mpg ~ ., data = mtcars)
#> parsnip model object
#>
#>
#> Call:
#> randomForest(x = maybe_data_frame(x), y = y, ntree = ~2000, mtry = min_cols(~4, x))
#> Type of random forest: regression
#> Number of trees: 2000
#> No. of variables tried at each split: 4
#>
#> Mean of squared residuals: 5.52
#> % Var explained: 84.3
Note that the call objects show num.trees = ~2000
. The tilde is the consequence of parsnip
using quosures to process the model specification’s arguments.
Normally, when a function is executed, the function’s arguments are immediately evaluated. In the case of parsnip
, the model specification’s arguments are not; the expression is captured along with the environment where it should be evaluated. That is what a quosure does.
parsnip
uses these expressions to make a model fit call that is evaluated. The tilde in the call above reflects that the argument was captured using a quosure.