details_C5_rules_C5.0: C5.0 rule-based classification models

details_C5_rules_C5.0R Documentation

C5.0 rule-based classification models


C50::C5.0() fits a model that derives feature rules from a tree for prediction. A single tree or boosted ensemble can be used. rules::c5_fit() is a wrapper around this function.


For this engine, there is a single mode: classification

Tuning Parameters

This model has 2 tuning parameters:

  • trees: # Trees (type: integer, default: 1L)

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

Note that C5.0 has a tool for early stopping during boosting where less iterations of boosting are performed than the number requested. C5_rules() turns this feature off (although it can be re-enabled using C50::C5.0Control()).

Translation from parsnip to the underlying model call (classification)

The rules extension package is required to fit this model.


  trees = integer(1),
  min_n = integer(1)
) %>%
  set_engine("C5.0") %>%
  set_mode("classification") %>%
## C5.0 Model Specification (classification)
## Main Arguments:
##   trees = integer(1)
##   min_n = integer(1)
## Computational engine: C5.0 
## Model fit template:
## rules::c5_fit(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
##     trials = integer(1), minCases = integer(1))

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

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

Saving fitted model objects

This model object contains data that are not required to make predictions. When saving the model for the purpose of prediction, the size of the saved object might be substantially reduced by using functions from the butcher package.


  • Quinlan R (1992). “Learning with Continuous Classes.” Proceedings of the 5th Australian Joint Conference On Artificial Intelligence, pp. 343-348.

  • Quinlan R (1993).”Combining Instance-Based and Model-Based Learning.” Proceedings of the Tenth International Conference on Machine Learning, pp. 236-243.

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

parsnip documentation built on June 24, 2024, 5:14 p.m.