convertOMLDataSetToMlr(obj, mlr.task.id = "<oml.data.name>",
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
Examples
# \dontrun{
# library("mlr")
# autosOML = getOMLDataSet(data.id = 9)
# autosMlr = convertOMLDataSetToMlr(autosOML)
# }