mlr3pipelines
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Dataflow Programming for Machine Learning in R
I think a relatively common user error (at least for not so experienced users) is that they forget the prefixes when setting graph parameters: (e.g. `nrounds` instead of `classif.xgboost.nrounds` below....
Many of our pipeops fit this mold: impute, encode, YeoJohnson, BoxCox (?), binning. Should PipeOpTaskPreproc(Simple), possibly both.
Basically https://github.com/mlr-org/mlr3pipelines/issues/677 again. Currently we naively turn `NA` into the `.__MISSING__` level for factor columns. If things are only missing during prediction, this introduces a new factor level. In the...
## description When using multisession with future, graph learner containing `colapply` raises an error, which never exists before and exists in sequential mode. ## error message ``` > # not...
It would be good if it were possible to specify multiple different allowed `task_type`s to be displayed in `input.type.train`, `input.type.predict`, `output.type.train`, and `output.type.predict`, when calling the S3 method `as.data.table.DictionaryPipeOp` for...
@mb706 check if you want/like this, this standardises to use more `ps$set_values()` like in the other mlr3 packages
Nothing to merge yet but a poc we should discuss. `mlr3tuning` is ready to use hotstarting efficiently and @sebffischer wants to use hotstarting for torch. This currently only works for...
`mlr3`'s `$configure()` allows to set both hyperparameters and fields. It would be nice if `GraphLearner` provided its own `$configure()` method that also allows to set fields of `PipeOp`s, something like...