Comments (2)
First we will do it the R6 way - it will be more complicated but more flexible. In the future there might be helper functions but not now.
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In the future there might be helper functions
not for what is discussed here.
what you see above are 2 things:
a) how do i create an object --> R6 constructor. it is not our job, to help people who refuse to read what that means. (and actually: the old mlr was worse here! because it was LESS clear how an object was constructed. i just created a convention that these types of functions should start with "make"...)
b) creation of an backend. yes it is one line more, but now you can work with other stuff than dataframes.
one extra line vs. totally cool new feature --> winner clear
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Related Issues (20)
- Measure's check_prerequisites is ignored when calling `$score()` on a ResampleResult
- NumFOCUS funding HOT 1
- ResampleResult and BenchmarkResult's `$score()` behave surprisingly when passing a `predict_set`
- Release mlr3 0.18.0
- Example task with non-standard primary key
- Task cbind breaks when task's backend has primary_key different to `..row_id` HOT 3
- error message when using examples from mlr3 book HOT 1
- Save only selected edges in graph learner HOT 4
- Release mlr3 0.17.0
- "classif.svm" and "classif.regr" not in the key of as.data.table(mlr_learners) HOT 2
- fallback learner should maybe be a warning HOT 1
- Error in benchmark_grid A Resampling is instantiated for a task with a different number of observations HOT 4
- why mlr3 randomforest importance is different from randomForest package HOT 2
- i am sorry i do not know how to delete it
- who is author of Resampling? HOT 6
- Release mlr3 0.17.1
- resample() does not set data_prototype (and task_prototype), which some learners rely on HOT 6
- get column names used to train a learner? HOT 2
- Measure Documentations could be improved
- predict_time can be (kind of) wrong
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