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C50::C5.0() creates a series of classification trees forming an ensemble. Each tree depends on the results of previous trees. All trees in the ensemble are combined to produce a final prediction.


For this engine, there is a single mode: classification

Tuning Parameters

This model has 3 tuning parameters:

  • trees: # Trees (type: integer, default: 15L)

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

  • sample_size: Proportion Observations Sampled (type: double, default: 1.0)

The implementation of C5.0 limits the number of trees to be between 1 and 100.

Translation from parsnip to the original package (classification)

boost_tree(trees = integer(), min_n = integer(), sample_size = numeric()) %>% 
  set_engine("C5.0") %>% 
  set_mode("classification") %>% 

## Boosted Tree Model Specification (classification)
## Main Arguments:
##   trees = integer()
##   min_n = integer()
##   sample_size = numeric()
## Computational engine: C5.0 
## Model fit template:
## parsnip::C5.0_train(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
##     trials = integer(), minCases = integer(), sample = numeric())

C5.0_train() is a wrapper around C50::C5.0() that makes it easier to run this model.

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.

Other details

Early stopping

By default, early stopping is used. To use the complete set of boosting iterations, pass earlyStopping = FALSE to set_engine(). Also, it is unlikely that early stopping will occur if sample_size = 1.


The “Fitting and Predicting with parsnip” article contains examples for boost_tree() with the "C5.0" engine.


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