Uses a logistic regression model to calibrate probabilities

Uses a logistic regression model to calibrate probabilities

Usage

cal_estimate_logistic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  smooth = TRUE,
  parameters = NULL,
  ...
)

# S3 method for data.frame
cal_estimate_logistic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  smooth = TRUE,
  parameters = NULL,
  ...,
  .by = NULL
)

# S3 method for tune_results
cal_estimate_logistic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  smooth = TRUE,
  parameters = NULL,
  ...
)

# S3 method for grouped_df
cal_estimate_logistic(
  .data,
  truth = NULL,
  estimate = NULL,
  smooth = TRUE,
  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.

smooth

Applies to the logistic models. It switches between logistic spline when TRUE, and simple logistic regression when FALSE.

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 existing modeling functions from other packages to create the calibration:

• stats::glm() is used when smooth is set to FALSE

• mgcv::gam() is used when smooth is set to TRUE

Multiclass Extension

This method has not been extended to multiclass outcomes. However, the natural multiclass extension is cal_estimate_multinomial().

See also

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

Examples

# It will automatically identify the probability columns
# if passed a model fitted with tidymodels
cal_estimate_logistic(segment_logistic, Class)
#> 
#> ── Probability Calibration 
#> Method: Generalized additive model
#> 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_logistic(segment_logistic, Class, c(.pred_poor, .pred_good))
#> 
#> ── Probability Calibration 
#> Method: Generalized additive model
#> 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_logistic(segment_logistic, Class, dplyr::starts_with(".pred_"))
#> 
#> ── Probability Calibration 
#> Method: Generalized additive model
#> Type: Binary
#> Source class: Data Frame
#> Data points: 1,010
#> Truth variable: `Class`
#> Estimate variables:
#> `.pred_good` ==> good
#> `.pred_poor` ==> poor