cal_estimate_none: Do not calibrate model predictions.

In topepo/probably: Tools for Post-Processing Predicted Values

Do not calibrate model predictions.

Description

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

identical(
  cal_apply(boosting_predictions_oob, nada),
  boosting_predictions_oob
)

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

nichts <- cal_estimate_none(segment_logistic, Class)

identical(
  cal_apply(segment_logistic, nichts),
  segment_logistic
)

topepo/probably documentation built on June 8, 2025, 4:23 a.m.