Comments (3)
and sklearn
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Sounds to me like having a workflow or similar object to combine learner and model would be better. It's not clear to me how a learner with state would fit into the current framework. For example, what happens when a learner with a model is wrapped? Model removed or automatically retrained? Same with changing hyperparameters for a learner.
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we already decided this for the new framework. it makes the new API better.
you can see this when we document this a bit better.
short answer:
Sounds to me like having a workflow or similar object to combine learner and model would be better.
we do this. the object "Learner" now combines calls regarding training, predictions and model. this makes everything simpler and more natural.
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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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