mlr: getCaretParamSet – R documentation

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getCaretParamSet

Get tuning parameters from a learner of the caret R-package.

Description

Constructs a grid of tuning parameters from a learner of the caret R-package. These values are then converted into a list of non-tunable parameters (par.vals) and a tunable ParamHelpers::ParamSet (par.set), which can be used by tuneParams for tuning the learner. Numerical parameters will either be specified by their lower and upper bounds or they will be discretized into specific values.

Usage

getCaretParamSet(learner, length = 3L, task, discretize = TRUE)

Arguments

`learner`	(`character(1)`) The name of the learner from `caret` (cf. https://topepo.github.io/caret/available-models.html). Note that the names in `caret` often differ from the ones in `mlr`.
`length`	(`integer(1)`) A length / precision parameter which is used by `caret` for generating the grid of tuning parameters. `caret` generates either as many values per tuning parameter / dimension as defined by `length` or only a single value (in case of non-tunable `par.vals`).
`task`	(Task) Learning task, which might be requested for creating the tuning grid.
`discretize`	(`logical(1)`) Should the numerical parameters be discretized? Alternatively, they will be defined by their lower and upper bounds. The default is `TRUE`.

Value

(list(2)). A list of parameters:

par.vals contains a list of all constant tuning parameters
par.set is a ParamHelpers::ParamSet, containing all the configurable tuning parameters

Examples

if (requireNamespace("caret") && requireNamespace("mlbench")) {
  library(caret)
  classifTask = makeClassifTask(data = iris, target = "Species")

  # (1) classification (random forest) with discretized parameters
  getCaretParamSet("rf", length = 9L, task = classifTask, discretize = TRUE)

  # (2) regression (gradient boosting machine) without discretized parameters
  library(mlbench)
  data(BostonHousing)
  regrTask = makeRegrTask(data = BostonHousing, target = "medv")
  getCaretParamSet("gbm", length = 9L, task = regrTask, discretize = FALSE)
}

mlr

Machine Learning in R

v2.19.0

BSD_2_clause + file LICENSE

Authors

Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>), Lars Kotthoff [aut], Patrick Schratz [aut, cre] (<https://orcid.org/0000-0003-0748-6624>), Julia Schiffner [aut], Jakob Richter [aut], Zachary Jones [aut], Giuseppe Casalicchio [aut] (<https://orcid.org/0000-0001-5324-5966>), Mason Gallo [aut], Jakob Bossek [ctb] (<https://orcid.org/0000-0002-4121-4668>), Erich Studerus [ctb] (<https://orcid.org/0000-0003-4233-0182>), Leonard Judt [ctb], Tobias Kuehn [ctb], Pascal Kerschke [ctb] (<https://orcid.org/0000-0003-2862-1418>), Florian Fendt [ctb], Philipp Probst [ctb] (<https://orcid.org/0000-0001-8402-6790>), Xudong Sun [ctb] (<https://orcid.org/0000-0003-3269-2307>), Janek Thomas [ctb] (<https://orcid.org/0000-0003-4511-6245>), Bruno Vieira [ctb], Laura Beggel [ctb] (<https://orcid.org/0000-0002-8872-8535>), Quay Au [ctb] (<https://orcid.org/0000-0002-5252-8902>), Martin Binder [ctb], Florian Pfisterer [ctb], Stefan Coors [ctb], Steve Bronder [ctb], Alexander Engelhardt [ctb], Christoph Molnar [ctb], Annette Spooner [ctb]

Initial release