Comments (4)
I suggest using this dataset (5000 obs, 1k features)
from mlr3filters.
Are you suggesting it because of its dimension or because filtering works pretty well on it?
What about spam in mlr3?
from mlr3filters.
spam
would also work I'd say. In the end the question is what the use case should show:
- compare filters against wrappers?
- compare single filters against ensemble filters?
- showcase the caching attribute of filters?
None of the above do really depend on the dataset unless you want to find a use case where features outperform wrappers.
from mlr3filters.
Yes, my idea was because there are so many features.
I would prefer a regression task (because then I can use it for MaRDI), that would make spam less attractive for me.
Are there already benchmark studies on those questions @pat-s ?
In this case we could make a gallery-posts that gives some recommendations on what to do with filters?
I.e. giving the reader the answer to those questions :)
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Related Issues (20)
- Cross-link mlr3pipelines HOT 1
- Survival filters of MLR to be available in MLR3 HOT 5
- Filters and mlr3pipelines and filters before building graph HOT 1
- `FilterFindCorrelation` needs adjustment after latest {pillar} update HOT 2
- FilterPerformance should not have default "classif.featureless" for the learner HOT 8
- `FilterCarSurvScore` and mlr3proba CRAN removal HOT 8
- praznik filters throw errors HOT 1
- Improve handling of missing values
- mlr3filters 0.7.0 breaks mlr3spatiotempcv HOT 1
- Filter carscore integer features HOT 1
- Add 'surv' task_type in `FindCorrelation` filter HOT 1
- Implement hash / phash
- importance filter default is bad HOT 3
- Univariate Cox filter for survival models
- Calculating hash is broken
- Release mlr3filters 0.8.0
- Improve internals of FilterPermutation HOT 2
- Is it possible to add ks value in mlr3filters? HOT 3
- assert task type prior to calculating
- typo in FilterPermutation help
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