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Ordinal decision trees via CART

Source: R/decision_tree_rpartScore.R
details_decision_tree_rpartScore.Rd

rpartScore::rpartScore() extends rpart to fit classification trees for ordinal responses.

Details

For this engine, there is a single mode: classification

Tuning parameters

This model has 3 tuning parameters:

  • tree_depth: Tree Depth (type: integer, default: 30L)

  • min_n: Minimal Node Size (type: integer, default: 2L)

  • cost_complexity: Cost-Complexity Parameter (type: double, default: 0.01)

Translation from parsnip to the original package

decision_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1)) |>
  set_engine("rpartScore") |>
  set_mode("classification") |>
  translate()

## Decision Tree Model Specification (classification)
##
## Main Arguments:
##   cost_complexity = double(1)
##   tree_depth = integer(1)
##   min_n = integer(1)
##
## Computational engine: rpartScore
##
## Model fit template:
## ordered::rpartScore_wrapper(formula = missing_arg(), data = missing_arg(),
##     weights = missing_arg(), cp = double(1), maxdepth = integer(1),
##     minsplit = min_rows(0L, data))

Preprocessing requirements

This engine does not require any special encoding of the predictors. Categorical predictors can be partitioned into groups of factor levels (e.g. {a, c} vs {b, d}) when splitting at a node. Dummy variables are not required for this model.

Case weights

This model can utilize case weights during model fitting. To use them, see the documentation in case_weights and the examples on tidymodels.org.

The fit() and fit_xy() arguments have arguments called case_weights that expect vectors of case weights.

Prediction types

parsnip:::get_from_env("decision_tree_predict") |>
  dplyr::filter(engine == "rpartScore") |>
  dplyr::select(mode, type)

## # A tibble: 1 x 2
##   mode           type
##   <chr>          <chr>
## 1 classification class

References

  • Galimberti G, Soffritti G, Di Maso M. 2012. Classification Trees for Ordinal Responses in R: The rpartScore Package. Journal of Statistical Software 47(10):1-25. .

  • Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.