Multi-calibration pipeline — ppl

Wraps MCBoost in a Pipeline to be used with mlr3pipelines. For now this assumes training on the same dataset that is later used for multi-calibration.

ppl_mcboost(learner = lrn("classif.featureless"), param_vals = list())

Arguments

learner: (mlr3)mlr3::Learner
Initial learner. Internally wrapped into a PipeOpLearnerCV with resampling.method = "insample" as a default. All parameters can be adjusted through the resulting Graph's param_set. Defaults to lrn("classif.featureless"). Note: An initial predictor can also be supplied via the init_predictor parameter.
param_vals: list
List of parameter values passed on to MCBoost$new.

Value

(mlr3pipelines) Graph

Examples

  if (FALSE) {
  library("mlr3pipelines")
  gr = ppl_mcboost()
  }