tests/testthat/_snaps/cal-estimate-isotonic.md

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

Isotonic estimates work - data.frame

Code
  print(sl_isotonic)
Message

  -- Probability Calibration 
  Method: Isotonic regression calibration
  Type: Binary
  Source class: Data Frame
  Data points: 1,010
  Unique Predicted Values: 78
  Truth variable: `Class`
  Estimate variables:
  `.pred_good` ==> good
  `.pred_poor` ==> poor

Code
  print(sl_isotonic_group)
Message

  -- Probability Calibration 
  Method: Isotonic regression calibration
  Type: Binary
  Source class: Data Frame
  Data points: 1,010, split in 2 groups
  Unique Predicted Values: 77
  Truth variable: `Class`
  Estimate variables:
  `.pred_good` ==> good
  `.pred_poor` ==> poor

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

Isotonic estimates work - tune_results

Code
  print(tl_isotonic)
Message

  -- Probability Calibration 
  Method: Isotonic regression calibration
  Type: Binary
  Source class: Tune Results
  Data points: 4,000, split in 8 groups
  Unique Predicted Values: 92
  Truth variable: `class`
  Estimate variables:
  `.pred_class_1` ==> class_1
  `.pred_class_2` ==> class_2

Code
  print(mtnl_isotonic)
Message

  -- Probability Calibration 
  Method: Isotonic regression calibration
  Type: Multiclass (1 v All)
  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

Isotonic estimates errors - grouped_df

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

Isotonic linear estimates work - data.frame

Code
  print(sl_logistic)
Message

  -- Probability Calibration 
  Method: Isotonic regression calibration
  Type: Regression
  Source class: Data Frame
  Data points: 2,000
  Unique Predicted Values: 47
  Truth variable: `outcome`
  Estimate variables:
  `.pred` ==> predictions

Code
  print(sl_logistic_group)
Message

  -- Probability Calibration 
  Method: Isotonic regression calibration
  Type: Regression
  Source class: Data Frame
  Data points: 2,000, split in 10 groups
  Unique Predicted Values: 18
  Truth variable: `outcome`
  Estimate variables:
  `.pred` ==> predictions

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

Isotonic Bootstrapped estimates work - data.frame

Code
  print(sl_boot)
Message

  -- Probability Calibration 
  Method: Bootstrapped isotonic regression calibration
  Type: Binary
  Source class: Data Frame
  Data points: 1,010
  Truth variable: `Class`
  Estimate variables:
  `.pred_good` ==> good
  `.pred_poor` ==> poor

Code
  print(sl_boot_group)
Message

  -- Probability Calibration 
  Method: Bootstrapped isotonic regression calibration
  Type: Binary
  Source class: Data Frame
  Data points: 1,010, split in 2 groups
  Truth variable: `Class`
  Estimate variables:
  `.pred_good` ==> good
  `.pred_poor` ==> poor

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

Isotonic Bootstrapped estimates work - tune_results

Code
  print(tl_isotonic)
Message

  -- Probability Calibration 
  Method: Bootstrapped isotonic regression 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(mtnl_isotonic)
Message

  -- Probability Calibration 
  Method: Bootstrapped isotonic regression calibration
  Type: Multiclass (1 v All)
  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

Isotonic Bootstrapped estimates errors - 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.