Comments (5)
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Or making sure that you resample it to make sure.
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Thanks, Alex and Max! Could you guys provide an example of how to train embedding by resampling?
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You would train in the usual way but resample the recipe to make sure that you are not overfitting. If you are not tuning, then you could use tune::fit_resamples()
. Otherwise, tune::tune_grid()
would be a good choice.
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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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