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resample.md

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failure in recipe is caught elegantly

Code
  result <- fit_resamples(lin_mod, rec, folds, control = control)
Message
  x Fold1: preprocessor 1/1:
    Error in `step_ns()`:
    Caused by error in `if (...) NULL`:
    ! missing value where TRUE/FALSE needed
  x Fold2: preprocessor 1/1:
    Error in `step_ns()`:
    Caused by error in `if (...) NULL`:
    ! missing value where TRUE/FALSE needed
Condition
  Warning:
  All models failed. Run `show_notes(.Last.tune.result)` for more information.

failure in variables tidyselect specification is caught elegantly

Code
  result <- fit_resamples(workflow, folds, control = control)
Message
  x Fold1: preprocessor 1/1:
    Error in `fit()`:
    ! Can't select columns that don't exist.
    x Column `foobar` doesn't exist.
  x Fold2: preprocessor 1/1:
    Error in `fit()`:
    ! Can't select columns that don't exist.
    x Column `foobar` doesn't exist.
Condition
  Warning:
  All models failed. Run `show_notes(.Last.tune.result)` for more information.

classification models generate correct error message

Code
  result <- fit_resamples(log_mod, rec, folds, control = control)
Message
  x Fold1: preprocessor 1/1, model 1/1:
    Error in `check_outcome()`:
    ! For a classification model, the outcome should be a `factor`, not a ...
  x Fold2: preprocessor 1/1, model 1/1:
    Error in `check_outcome()`:
    ! For a classification model, the outcome should be a `factor`, not a ...
Condition
  Warning:
  All models failed. Run `show_notes(.Last.tune.result)` for more information.

tune_grid() falls back to fit_resamples() - formula

Code
  result <- tune_grid(lin_mod, mpg ~ ., folds)
Condition
  Warning:
  No tuning parameters have been detected, performance will be evaluated using the resamples with no tuning. Did you want to [tune()] parameters?

tune_grid() falls back to fit_resamples() - workflow variables

Code
  result <- tune_grid(wf, folds)
Condition
  Warning:
  No tuning parameters have been detected, performance will be evaluated using the resamples with no tuning. Did you want to [tune()] parameters?

tune_grid() ignores grid if there are no tuning parameters

Code
  result <- lin_mod %>% tune_grid(mpg ~ ., grid = data.frame(x = 1), folds)
Condition
  Warning:
  No tuning parameters have been detected, performance will be evaluated using the resamples with no tuning. Did you want to [tune()] parameters?

cannot autoplot fit_resamples() results

Code
  autoplot(result)
Condition
  Error in `autoplot()`:
  ! There is no `autoplot()` implementation for `resample_results`.

ellipses with fit_resamples

Code
  lin_mod %>% fit_resamples(mpg ~ ., folds, something = "wrong")
Condition
  Warning:
  The `...` are not used in this function but one or more objects were passed: 'something'
Output
  # Resampling results
  # 2-fold cross-validation 
  # A tibble: 2 x 4
    splits          id    .metrics         .notes          
    <list>          <chr> <list>           <list>          
  1 <split [16/16]> Fold1 <tibble [2 x 4]> <tibble [0 x 4]>
  2 <split [16/16]> Fold2 <tibble [2 x 4]> <tibble [0 x 4]>

argument order gives errors for recipe/formula

Code
  fit_resamples(rec, lin_mod, folds)
Condition
  Error in `fit_resamples()`:
  ! The first argument to [fit_resamples()] should be either a model or workflow.

Code
  fit_resamples(mpg ~ ., lin_mod, folds)
Condition
  Error in `fit_resamples()`:
  ! The first argument to [fit_resamples()] should be either a model or workflow.

retain extra attributes

Code
  fit_resamples(lin_mod, recipes::recipe(mpg ~ ., mtcars[rep(1:32, 3000), ]),
  folds, control = control_resamples(save_workflow = TRUE))
Message
  i The workflow being saved contains a recipe, which is 8.07 Mb in i memory. If
  this was not intentional, please set the control setting i `save_workflow =
  FALSE`.
Output
  # Resampling results
  # 2-fold cross-validation 
  # A tibble: 2 x 4
    splits          id    .metrics         .notes          
    <list>          <chr> <list>           <list>          
  1 <split [16/16]> Fold1 <tibble [2 x 4]> <tibble [0 x 4]>
  2 <split [16/16]> Fold2 <tibble [2 x 4]> <tibble [0 x 4]>

fit_resamples() when objects need tuning

2 arguments have been tagged for tuning in these components: model_spec and recipe. 
Please use one of the tuning functions (e.g. `tune_grid()`) to optimize them.

1 argument has been tagged for tuning in this component: model_spec. 
Please use one of the tuning functions (e.g. `tune_grid()`) to optimize them.

1 argument has been tagged for tuning in this component: recipe. 
Please use one of the tuning functions (e.g. `tune_grid()`) to optimize them.