runTaskMlr function

Run mlr learner on OpenML task.

Run mlr learner on OpenML task.

Run task with a specified learner from mlr and produce predictions. By default, the evaluation measure contained in the task is used.

runTaskMlr( task, learner, measures = NULL, verbosity = NULL, seed = 1, scimark.vector = NULL, models = TRUE, ... )

Arguments

  • task: [OMLTask]

    An OpenML task.

  • learner: [Learner]

    Learner from package mlr to run the task.

  • measures: [Measure]

    Additional measures that should be computed.

  • verbosity: [integer(1)]

    Print verbose output on console? Possible values are:

    0: normal output,

    1: info output,

    2: debug output.

    Default is set via setOMLConfig.

  • seed: [numeric(1)|OMLSeedParList ]

    Set a seed to make the run reproducible. Default is 1 and sets the seed using set.seed(1).

  • scimark.vector: [numeric(6)]

    Optional vector of performance measurements computed by the scientific SciMark benchmark. May be computed using the rscimark R package. Default is NULL, which means no performance measurements.

  • models: [logical(1)]

    This argument is passed to benchmark. Should all fitted models be stored in the ResampleResult? Default is TRUE.

  • ...: [any]

    Further arguments that are passed to convertOMLTaskToMlr.

Returns

[list] Named list with the following components:

  • run: The OMLRun object.
  • bmr: Benchmark result returned by benchmark.
  • flow: The generated OMLFlow object.

Examples

# \dontrun{ # library(mlr) # ## run a single flow (learner) on a single task # task = getOMLTask(57) # lrn = makeLearner("classif.rpart") # res = runTaskMlr(task, lrn) # ## the result "res" is a list, storing information on the actual "run", the # ## corresponding benchmark result "bmr" and the applied "flow" # }

See Also

getOMLTask, makeLearner

  • Maintainer: Giuseppe Casalicchio
  • License: BSD_3_clause + file LICENSE
  • Last published: 2022-10-19