README.md

parsnip

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status CRAN
status Downloads lifecycle

Introduction

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.

Installation

# 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")

Getting started

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 goals of parsnip are to:

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:

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.

Contributing

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.



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parsnip documentation built on Nov. 11, 2022, 9:09 a.m.