decision_tree-tunable.md
In parsnip: A Common API to Modeling and Analysis Functions

tunable parameters for decision_tree + rpart

Code
  display_tunable_call_info(set_engine(decision_tree(), "rpart"))
Output
  tree_depth                | pkg: dials, fun: tree_depth | main
  min_n                     | pkg: dials, fun: min_n | main
  cost_complexity           | pkg: dials, fun: cost_complexity | main

tunable parameters for decision_tree + C5.0

Code
  display_tunable_call_info(set_engine(decision_tree(), "C5.0"))
Output
  min_n                     | pkg: dials, fun: min_n | main
  CF                        | pkg: dials, fun: confidence_factor | engine
  noGlobalPruning           | pkg: dials, fun: no_global_pruning | engine
  winnow                    | pkg: dials, fun: predictor_winnowing | engine
  fuzzyThreshold            | pkg: dials, fun: fuzzy_thresholding | engine
  bands                     | pkg: dials, fun: rule_bands | engine

tunable parameters for decision_tree + spark

Code
  display_tunable_call_info(set_engine(decision_tree(), "spark"))
Output
  tree_depth                | pkg: dials, fun: tree_depth | main
  min_n                     | pkg: dials, fun: min_n | main

tunable parameters for decision_tree + partykit

Code
  display_tunable_call_info(set_engine(decision_tree(), "partykit"))
Output
  mincriterion              | pkg: dials, fun: conditional_min_criterion | engine
  teststat                  | pkg: dials, fun: conditional_test_statistic | engine
  testtype                  | pkg: dials, fun: conditional_test_type | engine