Comments (6)
That has nothing to do with the learner. A re-sampling also has parameter values (iterations etc)
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@jakob-r your answer does not make sense.
the point is this:
for is the abstract interface, that a class has to provide so i can call "resample" on it?
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(because that is exactly what i need to know so i can write a pipeline...)
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@mllg Do you know what @berndbischl means?
@berndbischl Can you maybe link to the code that you mean?
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Solved via hangout.
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was it really solved? it is documented now somewhere? we really need this to move forward with the piplines
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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
- columns that are not present during prediction that are not targets
- Feature Request: Predict type `"ci"` in addition to `"se"` HOT 1
- `$score()` has surprising behaviour when passing the argument `predict_sets`
- 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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