cal_estimate_beta {probably}

Uses a Beta calibration model to calculate new probabilities

Description

Uses a Beta calibration model to calculate new probabilities

Usage

cal_estimate_beta(
  .data,
  truth = NULL,
  shape_params = 2,
  location_params = 1,
  estimate = dplyr::starts_with(".pred_"),
  parameters = NULL,
  ...
)

## S3 method for class 'data.frame'
cal_estimate_beta(
  .data,
  truth = NULL,
  shape_params = 2,
  location_params = 1,
  estimate = dplyr::starts_with(".pred_"),
  parameters = NULL,
  ...,
  .by = NULL
)

## S3 method for class 'tune_results'
cal_estimate_beta(
  .data,
  truth = NULL,
  shape_params = 2,
  location_params = 1,
  estimate = dplyr::starts_with(".pred_"),
  parameters = NULL,
  ...
)

## S3 method for class 'grouped_df'
cal_estimate_beta(
  .data,
  truth = NULL,
  shape_params = 2,
  location_params = 1,
  estimate = NULL,
  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.

shape_params

Number of shape parameters to use. Accepted values are 1 and 2. Defaults to 2.

location_params

Number of location parameters to use. Accepted values 1 and 0. Defaults to 1.

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.

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 the betacal::beta_calibration() function, and retains the resulting model.

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.

References

Meelis Kull, Telmo M. Silva Filho, Peter Flach "Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration," Electronic Journal of Statistics 11(2), 5052-5080, (2017)

See Also

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

Examples

# It will automatically identify the probability columns
# if passed a model fitted with tidymodels
cal_estimate_beta(segment_logistic, Class)