Uses a support vector regression model to calibrate numeric predictions

Uses a support vector regression model to calibrate numeric predictions

Usage

cal_estimate_svm(
  .data,
  truth = NULL,
  estimate = dplyr::matches("^.pred$"),
  smooth = TRUE,
  parameters = NULL,
  ...,
  .by = NULL
)

# S3 method for class 'data.frame'
cal_estimate_svm(
  .data,
  truth = NULL,
  estimate = dplyr::matches("^.pred$"),
  smooth = TRUE,
  parameters = NULL,
  ...,
  .by = NULL
)

# S3 method for class 'tune_results'
cal_estimate_svm(
  .data,
  truth = NULL,
  estimate = dplyr::matches("^.pred$"),
  smooth = TRUE,
  parameters = NULL,
  ...
)

# S3 method for class 'grouped_df'
cal_estimate_svm(
  .data,
  truth = NULL,
  estimate = NULL,
  smooth = TRUE,
  parameters = NULL,
  ...
)

Arguments

.data

An ungrouped data.frame object, or tune_results object, that contains a prediction column.

truth

The column identifier for the observed outcome data (that is numeric). This should be an unquoted column name.

estimate

Column identifier for the predicted values

smooth

Applies to the svm models. It switches between a polydotTRUE, and vanilladot 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 predictions.

Details

This function uses existing modeling functions from other packages to create the calibration:

• kernlab::ksvm() with a "vanilladot" is used when smooth is set to FALSE

• kernlab::ksvm() with a "polydot" is used when smooth is set to TRUE

These methods estimate the relationship in the unmodified predicted values and then remove that trend when cal_apply() is invoked.

See also

https://www.tidymodels.org/learn/models/calibration/,