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 ofranger::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 thattrees
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
#>
#> EngineSpecific 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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