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)
- Docs: better documentation how to suppress learner output HOT 2
- Resampling: cv-plus HOT 1
- set_col_roles has confusing error for user-defined roles HOT 10
- Release mlr3 0.20.0
- How to predict in new data in mlr3 0.20.0 HOT 3
- Retrieve data from learner HOT 6
- Release mlr3 0.20.1
- Licensing issue due to mlr3 dependnency RhpcBLASctl HOT 3
- Release mlr3 0.20.2
- Unexpected Feature Inclusion in mlr3 Task Definition HOT 4
- Allow Multiple Ratios in partition() for Creating Training, Testing, and Validation Sets HOT 2
- learner predict HOT 1
- `MeasurerElapsedTime` uses "mean" for aggregation the total time, while "sum" would be more appropriate HOT 1
- Accessing `col_info` is a bottleneck
- Set environment variables to disable parallelization
- remove some unneeded marshal tests
- Add FAQ on workaround to use model trained with old mlr3 version
- Add `set_internal_tuning()` helper method for configuring internal hyperparameter optimization
- Feature Request: Allow no required predict sets for a `Measure` HOT 1
- Issues with as.data.table(mlr_learners) Function HOT 2
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