Emil Hvitfeldt
Emil Hvitfeldt
I'm thinking specifically for `bake()`ing. We already ran into this a couple of times for some steps. We should add to the base testing suite It will take forever to...
This should shortcut its ability to rename, and be more inline with other `check_*()` functions
This is a very unlikely thing to happen naturally, but I'm still posting as I may discover other weird things when investigating ```r library(recipes) exp_data prep() |> bake(new_data = NULL)...
# Tidyup: variable input/output information in {recipes} **Champion**: Emil **Co-Champion**: Max **Status**: Draft ## Abstract The [recipes](https://recipes.tidymodels.org/) provide a pipe-able and flexible way of processing data. Each operation is done...
The warning could be better handled on our end since it doesn't specify which variables are the problem. ``` r library(recipes) recipe(~., data = mtcars) |> step_spline_b(vs, am) |> prep()...
It is quite simple for an `NaN` to slip in, and it is almost unnoticeable. But it doesn't have to be. `sd_check()` already checks for zero variable, we can extend...
``` r library(recipes) recipe(~ mpg, data = mtcars) |> step_spline_nonnegative(mpg, deg_free = 5, degree = 6) |> prep() |> bake(new_data = NULL) #> Warning: #> # A tibble: 32 ×...
As the title says. I think it would be helpful to have some helper functions that would extract what variables got selected by a step and which variables were returned....
This comes with an hard to understand error message ``` r library(recipes) recipe(mpg ~ ., mtcars) |> step_nnmf_sparse(disp, drat) |> prep() #> Error in `step_nnmf_sparse()`: #> Caused by error in...
Should there be a catch for this particular error that gets produced in `step_dummy()` when it tries to calculate too big of a matrix? I realise this will eventually get...