Multilabel wrapper all

[giuseppec]

• we need to add a reference + short description in the note field for the measures
• we need manual tests for the measures like here: https://github.com/mlr-org/mlr/blob/master/tests/testthat/test_base_measures.R#L271 where we create a small matrix and compare the measure function with its expected output
• write 1-2 pages for the tutorial
• use checkLearner(, props = ...) instead of hasLearnerProperties(...)
• check styleguide (also check if the data type is mentioned in the API)
• set the order arg to NULL and explain that by default this does "random" sampling. Also, use a vector with label names instead of indices for the order arg.
• Make multilabel-randomForestSRC ready (see Multilabel randomForestSRC #991)
• implement Parallelization (Zachs PR parallel ensembles #615)
• Review paper and send it to the RJournal

[berndbischl]

please note that this PR #959 already contains generic code to test all basic multilabel RLearners
(learners_all style)

[giuseppec]

@PhilippPro could you help here and look at points 1-3 from the list above? I'll do 4-6.

[PhilippPro]

Yes. next week.