Do not calibrate model predictions.

Usage

cal_estimate_none(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  parameters = NULL,
  ...
)

# S3 method for class 'data.frame'
cal_estimate_none(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  parameters = NULL,
  ...,
  .by = NULL
)

# S3 method for class 'tune_results'
cal_estimate_none(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  parameters = NULL,
  ...
)

# S3 method for class 'grouped_df'
cal_estimate_none(.data, truth = NULL, 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 outcome results (that is factor or numeric). 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 or numeric predictions. It defaults to the prefix used by tidymodels (.pred_). For classification problems, 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 does nothing to the predictions. It is used as a reference when tuning over different calibration methods.

Examples


nada <- cal_estimate_none(boosting_predictions_oob, outcome, .pred)
nada
#> 
#> ── Regression Calibration 
#> Method: No calibration
#> Source class: Data Frame
#> Data points: 2,000
#> Truth variable: `outcome`
#> Estimate variable: `.pred`

identical(
  cal_apply(boosting_predictions_oob, nada),
  boosting_predictions_oob
)
#> [1] TRUE

# ------------------------------------------------------------------------------

nichts <- cal_estimate_none(segment_logistic, Class)

identical(
  cal_apply(segment_logistic, nichts),
  segment_logistic
)
#> [1] TRUE