Comments (4)
We now always return a data frame from the formula method when preparing and preprocessing.
04e400f
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We don't need an arg for the outcome type because we consistently return a tibble from all methods
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prepare()
currently still returns y
as whatever went in. We could use standardize()
first. This would match with recipes / formula interface.
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Related Issues (20)
- Release hardhat 1.2.0 HOT 1
- Pass through strings_as_factors arg HOT 3
- Problem with predicting on new data for character column HOT 5
- Problem with a formula with spaces in the name of a factor and indicators = "none" HOT 2
- `mold()` inconsistently preserves (with XY method) or ignores (with formula method) non-base vector classes HOT 2
- Do a pass over `validate_is()` with an eye towards performance HOT 1
- Avoid `as_tibble()` where possible HOT 2
- multi-outcomes support for `spruce_prob_multi` shall clarify input format for multiple `pred_levels` HOT 2
- Upkeep for hardhat HOT 1
- Regression in development version of hardhat when using sf objects HOT 3
- Release hardhat 1.3.0 HOT 1
- Dynamically calculate weights HOT 5
- Using a division on the left-hand side of a formula throws an "Interaction terms can't be specified on the LHS of `formula`" HOT 2
- importance weights not compatible with DALEXtra::model_profile HOT 2
- Less restrictive snapshot test for recipes HOT 1
- Release hardhat 1.3.1
- correct name of element in mold documentation
- Add `extract_postprocessor()` to docs for `extract_*()` functions
- Release hardhat 1.4.0
- Roadmap: hardhat support for sparse tibbles HOT 1
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