tests/testthat/_snaps/cal-estimate-none.md

In topepo/probably: Tools for Post-Processing Predicted Values

no calibration works - data.frame

Code
  print(nope_reg)
Message

  -- Regression Calibration 
  Method: No calibration
  Source class: Data Frame
  Data points: 2,000
  Truth variable: `outcome`
  Estimate variable: `.pred`

Code
  print(nope_reg_group)
Message

  -- Regression Calibration 
  Method: No calibration
  Source class: Data Frame
  Data points: 2,000, split in 2 groups
  Truth variable: `outcome`
  Estimate variable: `.pred`

x `.by` cannot select more than one column.
i The following columns were selected:
i group1 and group2

`...` must be empty.
x Problematic argument:
* smooth = TRUE

Code
  print(nope_binary)
Message

  -- Probability Calibration 
  Method: No calibration
  Type: Binary
  Source class: Data Frame
  Data points: 1,010
  Truth variable: `Class`
  Estimate variables:
  `.pred_good` ==> good
  `.pred_poor` ==> poor

The selectors in `estimate` resolves to 1 values (".pred_poor") but there are 2 class levels ("good" and "poor").

x `.by` cannot select more than one column.
i The following columns were selected:
i group1 and group2

Code
  print(nope_multi)
Message

  -- Probability Calibration 
  Method: No calibration
  Type: Multiclass
  Source class: Data Frame
  Data points: 110
  Truth variable: `Species`
  Estimate variables:
  `.pred_bobcat` ==> bobcat
  `.pred_coyote` ==> coyote
  `.pred_gray_fox` ==> gray_fox

x `.by` cannot select more than one column.
i The following columns were selected:
i group1 and group2

no calibration works - tune_results

Code
  print(nope_reg)
Message

  -- Regression Calibration 
  Method: No calibration
  Source class: Tune Results
  Data points: 750, split in 10 groups
  Truth variable: `outcome`
  Estimate variable: `.pred`

`...` must be empty.
x Problematic argument:
* do_something = FALSE

Code
  print(nope_binary)
Message

  -- Probability Calibration 
  Method: No calibration
  Type: Binary
  Source class: Tune Results
  Data points: 4,000, split in 8 groups
  Truth variable: `class`
  Estimate variables:
  `.pred_class_1` ==> class_1
  `.pred_class_2` ==> class_2

Code
  print(nope_multi)
Message

  -- Probability Calibration 
  Method: No calibration
  Type: Multiclass
  Source class: Tune Results
  Data points: 5,000, split in 10 groups
  Truth variable: `class`
  Estimate variables:
  `.pred_one` ==> one
  `.pred_two` ==> two
  `.pred_three` ==> three

no calibration fails - grouped_df

x This function does not work with grouped data frames.
i Apply `dplyr::ungroup()` and use the `.by` argument.

topepo/probably documentation built on June 8, 2025, 4:23 a.m.