OpenML (version 1.10)

convertOMLDataSetToMlr: Convert an OpenML data set to mlr task.

Description

Converts an OMLDataSet to a Task.

Usage

convertOMLDataSetToMlr(obj, mlr.task.id = "",
  task.type = NULL, target = obj$desc$default.target.attribute,
  ignore.flagged.attributes = TRUE, drop.levels = TRUE,
  fix.colnames = TRUE, verbosity = NULL)

Arguments

obj

[OMLDataSet] The object that should be converted.

mlr.task.id

[character(1)] Id string for Task object. The strings <oml.data.name>, <oml.data.id> and <oml.data.version> will be replaced by their respective values contained in the OMLDataSet object. Default is <oml.data.name>.

task.type

[character(1)] As we only pass the data set, we need to define the task type manually. Possible are: “Supervised Classification”, “Supervised Regression”, “Survival Analysis”. Default is NULL which means to guess it from the target column in the data set. If that is a factor or a logical, we choose classification. If it is numeric we choose regression. In all other cases an error is thrown.

target

[character] The target for the classification/regression task. Default is the default.target.attribute of the OMLDataSetDescription.

ignore.flagged.attributes

[logical(1)] Should those features that are listed in the data set description slot “ignore.attribute” be removed? Default is TRUE.

drop.levels

[logical(1)] Should empty factor levels be dropped in the data? Default is TRUE.

fix.colnames

[logical(1)] Should colnames of the data be fixed using make.names? Default is TRUE.

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.

Value

[Task].

See Also

Other data set-related functions: OMLDataSetDescription, OMLDataSet, convertMlrTaskToOMLDataSet, deleteOMLObject, getOMLDataSet, listOMLDataSets, tagOMLObject, uploadOMLDataSet

Examples

# NOT RUN {
# \dontrun{
# 	library("mlr")
# 	autosOML = getOMLDataSet(data.id = 9)
# 	autosMlr = convertOMLDataSetToMlr(autosOML)
# }
# }