Prediction intervals via conformal inference and QRSVM — int_conformal_quantile

To compute quantiles, this function uses Quantile SVM instead of probably's "int_conformal_quantile" QRF or classic quantile regression.

Usage

int_conformal_quantile_svm(object, ...)

# S3 method for class 'workflow'
int_conformal_quantile_svm(object, train_data, cal_data, level = 0.95, ...)

Arguments

object: A fitted workflows::workflow() object.
...: Options to pass to qrsvm::qrsvm()
train_data, cal_data: Data frames with the predictor and outcome data. train_data should be the same data used to produce object and cal_data is used to produce predictions (and residuals). If the workflow used a recipe, these should be the data that were inputs to the recipe (and not the product of a recipe).
level: The confidence level for the intervals.

Value

An object of class "int_conformal_quantile" containing the information to create intervals (which includes object). The predict() method is used to produce the intervals.

Details

based on the initial probably implementation with slight modification.

origin: https://github.com/tidymodels/probably/blob/HEAD/R/conformal_infer_quantile.R

for more information, visit: https://probably.tidymodels.org