parsnip
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tidymodels
A tidy unified interface to models
This tests the error message for trying to use `proportional_hazards()` with the `glmnet` engine _without_ a penalty value because that's the thing the `translate()` method for PH models does in...
Closes #776. Worth noting that this error message has changed since the issue was filed, following up on #769. ``` r library(parsnip) bag_tree %>% set_mode("classification") #> Error in UseMethod("set_mode"): no...
Copies `knitr::combine_words` and friends to close #785. Could you confirm this does the trick for you, @mdneuzerling?
## The problem Under certain conditions, installation of `parsnip` fails with the following error: ``` Error installing package 'parsnip': =================================== * installing *source* package 'parsnip' ... ** package 'parsnip' successfully...
Hello, I'm working on a PR to address #765. While doing so I ran into an issue with `will_make_matrix()`. Shouldn't the return value be True if `y` is a matrix...
When `mode == 'unknown'`, `inform_missing_implementation` searches available models for all modes. Also had to use `new_model_spec(check_missing_spec = FALSE)` in `boost_tree` (and perhaps other models that involve extension packages) and `new_model_spec(check_missing_spec...
Closes #774, closes #459. Related to #411. The goal of this PR is to ensure that folks can pass arguments that live in the `param` argument to `xgb.train`. The [new...
Some adjustments to effectively enable + document the `sample_size` argument for the lightgbm engine. Will be followed up by a PR in bonsai—1/2 to close tidymodels/bonsai#30. The gist: https://github.com/tidymodels/parsnip/blob/713d617274b54a9f99c5b67c2cf52a7b0a3e431c/man/rmd/boost_tree_lightgbm.Rmd#L79-L81 Will...
We need to include more details about using gam formula in the engine doc`gen_additive_mod(engien = "mgcv")`. The engine doc only shows model fitting examples when using gam formula in `fit()`...
## Feature The `weight` has been supported by `xgb_train`, but the workflow doesn't pass it the importance weights. ``` r library(tidymodels) library(hardhat) boost_tree_spec % set_mode(mode) %>% set_engine(engine, ...) } xgboost_recipe...
