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baguette::bagger() creates an collection of decision trees forming an ensemble. 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 1 tuning parameters:

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

Translation from parsnip to the original package (classification)

The baguette extension package is required to fit this model.


bag_tree(min_n = integer()) %>% 
  set_engine("C5.0") %>% 
  set_mode("classification") %>% 

## Bagged Decision Tree Model Specification (classification)
## Main Arguments:
##   cost_complexity = 0
##   min_n = integer()
## Computational engine: C5.0 
## Model fit template:
## baguette::bagger(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
##     minCases = integer(), base_model = "C5.0")

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.


  • Breiman, L. 1996. “Bagging predictors”. Machine Learning. 24 (2): 123-140

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