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Uses a bootstrapped Isotonic regression model to calibrate probabilities

Source: R/cal-estimate-isotonic.R
cal_estimate_isotonic_boot.Rd

Uses a bootstrapped Isotonic regression model to calibrate probabilities

Usage

cal_estimate_isotonic_boot(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  times = 10,
  parameters = NULL,
  ...
)

# S3 method for data.frame
cal_estimate_isotonic_boot(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  times = 10,
  parameters = NULL,
  ...,
  .by = NULL
)

# S3 method for tune_results
cal_estimate_isotonic_boot(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  times = 10,
  parameters = NULL,
  ...
)

# S3 method for grouped_df
cal_estimate_isotonic_boot(
  .data,
  truth = NULL,
  estimate = NULL,
  times = 10,
  parameters = NULL,
  ...
)

Arguments

.data

An ungrouped data.frame object, or tune_results object, that contains predictions and probability columns.

truth

The column identifier for the true class results (that is a factor). This should be an unquoted column name.

estimate

A vector of column identifiers, or one of dplyr selector functions to choose which variables contains the class probabilities. It defaults to the prefix used by tidymodels (.pred_). The order of the identifiers will be considered the same as the order of the levels of the truth variable.

times

Number of bootstraps.

parameters

(Optional) An optional tibble of tuning parameter values that can be used to filter the predicted values before processing. Applies only to tune_results objects.

...

Additional arguments passed to the models or routines used to calculate the new probabilities.

.by

The column identifier for the grouping variable. This should be a single unquoted column name that selects a qualitative variable for grouping. Default to NULL. When .by = NULL no grouping will take place.

Details

This function uses stats::isoreg() to create obtain the calibration values. It runs stats::isoreg() multiple times, and each time with a different seed. The results are saved inside the returned cal_object.

Multiclass Extension

This method is designed to work with two classes. For multiclass, it creates a set of "one versus all" calibrations for each class. After they are applied to the data, the probability estimates are re-normalized to add to one. This final step might compromise the calibration.

See also

https://www.tidymodels.org/learn/models/calibration/, cal_validate_isotonic_boot()

Examples

# It will automatically identify the probability columns
# if passed a model fitted with tidymodels
cal_estimate_isotonic_boot(segment_logistic, Class)
#> 
#> ── Probability Calibration 
#> Method: Bootstrapped isotonic regression
#> Type: Binary
#> Source class: Data Frame
#> Data points: 1,010
#> Truth variable: `Class`
#> Estimate variables:
#> `.pred_good` ==> good
#> `.pred_poor` ==> poor
# Specify the variable names in a vector of unquoted names
cal_estimate_isotonic_boot(segment_logistic, Class, c(.pred_poor, .pred_good))
#> 
#> ── Probability Calibration 
#> Method: Bootstrapped isotonic regression
#> Type: Binary
#> Source class: Data Frame
#> Data points: 1,010
#> Truth variable: `Class`
#> Estimate variables:
#> `.pred_good` ==> good
#> `.pred_poor` ==> poor
# dplyr selector functions are also supported
cal_estimate_isotonic_boot(segment_logistic, Class, dplyr::starts_with(".pred"))
#> 
#> ── Probability Calibration 
#> Method: Bootstrapped isotonic regression
#> Type: Binary
#> Source class: Data Frame
#> Data points: 1,010
#> Truth variable: `Class`
#> Estimate variables:
#> `.pred_good` ==> good
#> `.pred_poor` ==> poor