Applies a calibration to a set of existing predictions

Applies a calibration to a set of existing predictions

Usage

cal_apply(.data, object, pred_class = NULL, parameters = NULL, ...)

# S3 method for data.frame
cal_apply(.data, object, pred_class = NULL, parameters = NULL, ...)

# S3 method for tune_results
cal_apply(.data, object, pred_class = NULL, parameters = NULL, ...)

# S3 method for cal_object
cal_apply(.data, object, pred_class = NULL, parameters = NULL, ...)

Arguments

.data

An object that can process a calibration object.

object

The calibration object (cal_object).

pred_class

(Optional, classification only) Column identifier for the hard class predictions (a factor vector). This column will be adjusted based on changes to the calibrated probability columns.

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.

...

Optional arguments; currently unused.

Details

cal_apply() currently supports data.frames only. It extracts the truth and the estimate columns names from the calibration object.

See also

https://www.tidymodels.org/learn/models/calibration/, cal_estimate_beta(), cal_estimate_isotonic(), cal_estimate_isotonic_boot(), cal_estimate_linear(), cal_estimate_logistic(), cal_estimate_multinomial()

Examples

# ------------------------------------------------------------------------------
# classification example

w_calibration <- cal_estimate_logistic(segment_logistic, Class)

cal_apply(segment_logistic, w_calibration)
#> # A tibble: 1,010 × 3
#>    .pred_poor .pred_good Class
#>         <dbl>      <dbl> <fct>
#>  1      0.974     0.0258 poor 
#>  2      0.930     0.0700 poor 
#>  3      0.220     0.780  good 
#>  4      0.205     0.795  good 
#>  5      0.976     0.0244 poor 
#>  6      0.590     0.410  good 
#>  7      0.777     0.223  good 
#>  8      0.135     0.865  good 
#>  9      0.977     0.0231 poor 
#> 10      0.770     0.230  poor 
#> # ℹ 1,000 more rows