model-glmnet.md
In tidypredict: Run Predictions Inside the Database

returns the right output

Code
  rlang::expr_text(tf)
Output
  [1] "35.3137765116027 + (cyl * -0.871451193824228) + (hp * -0.0101173960249783) + \n    (wt * -2.59443677687505)"

formulas produces correct predictions

Code
  tidypredict_test(glmnet::glmnet(mtcars[, -1], mtcars$mpg, family = "gaussian",
  lambda = 1), mtcars[, -1])
Output
  tidypredict test results
  Difference threshold: 1e-12

   All results are within the difference threshold

Code
  tidypredict_test(glmnet::glmnet(mtcars[, -8], mtcars$vs, family = "binomial",
  lambda = 1), mtcars[, -1])
Output
  tidypredict test results
  Difference threshold: 1e-12

   All results are within the difference threshold

Code
  tidypredict_test(glmnet::glmnet(mtcars[, -8], mtcars$vs, family = "poisson",
  lambda = 1), mtcars[, -1])
Output
  tidypredict test results
  Difference threshold: 1e-12

   All results are within the difference threshold

errors if more than 1 penalty is selected

Code
  tidypredict_fit(model)
Condition
  Error in `parse_model()`:
  ! `tidypredict_fit()` requires that there are only 1 penalty selected, 79 were provided.

Code
  tidypredict_fit(model)
Condition
  Error in `parse_model()`:
  ! `tidypredict_fit()` requires that there are only 1 penalty selected, 2 were provided.

glmnet are handeld neatly with parsnip

Code
  rlang::expr_text(tf)
Output
  [1] "35.3140536966127 + (cyl * -0.871623418095165) + (hp * -0.0101157918502673) + \n    (wt * -2.59426484734253)"