Comments (1)
You're right about the 1st one. The 2nd one fails a brms
check at:
https://github.com/paul-buerkner/brms/blob/004edb522477d88dabe3815aae099b1211561076/R/data-response.R#L109-L111
In general, parsnip
requires a factor outcome for classification models while the engine fit function may support, or even require, numeric outcomes for the classification using certain families (e.g., ordinal categorical).
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Related Issues (16)
- Question about functions/aterms in formula HOT 2
- Set quantile parameter HOT 2
- Best Way to Parallelize Fitting HOT 1
- How to pass weights
- Add weights support
- Interested in collaboration to identify next set of feaures?
- predict() does not work with intercept-only models HOT 2
- model comparison with k-fold cross validation
- Clarify workflow of specifying and updating arguments HOT 11
- Revise engine-specific encodings HOT 4
- Improve test coverage
- Allow to specify non-linear models in the add_model interface HOT 3
- Support multivariate non-linear models HOT 6
- Support for tuning BRMS via tidymodels
- Error in `bayesian::bayesian_fit()`: ! Unsupported or invalid formula.override HOT 10
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