Comments (3)
This was previously discussed in tidymodels/recipes#399; I wasn't sold on what the poster wanted to return and they added a step function to their own package.
You might also want to take a look at tidymodels/planning#12. For non-preprocessing needs, I think that @kbodwin's thoughts are spot-on.
It would be good as long as the output is a factor variable that denotes the cluster that the sample belongs to. For new data, we can use a nearest centroid (or mediod) approach to assign new samples (but this is dependent on the clustering method).
I'd support this but don't have the time to do it; you'd have to start a PR.
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I'll close but please add a PR if this is important.
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This issue has been automatically locked. If you believe you have found a related problem, please file a new issue (with a reprex: https://reprex.tidyverse.org) and link to this issue.
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Related Issues (20)
- Metrice argument for step_umap function HOT 2
- Custom metric for step_umap HOT 2
- Upkeep for embed HOT 1
- remove tidyr_new_interface() check HOT 1
- Test that all tunable.step_*() are specified correctly HOT 1
- Use rlang errors HOT 1
- step_embed() should have `keep_original_cols` argument HOT 1
- Release embed 1.1.0 HOT 1
- Add missing infrastructure tests HOT 1
- Release embed 1.1.1 HOT 1
- new parameters for step_lencode_glm
- Release embed 1.1.2 HOT 1
- remove less_than_3.6 code in tests
- User defined cuts for tree-based methods
- step_discretize_kmeans()
- step_poisson_matric_factorization() HOT 1
- Steps idea: Dealing with correlation
- Release embed 1.1.3 HOT 1
- Release embed 1.1.4 HOT 1
- embed:::tunable.step_umap does not include target_weight in tunable params HOT 1
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