baguette::bagger() creates an collection of decision trees forming an
ensemble. All trees in the ensemble are combined to produce a final prediction.
Details
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.
library(baguette)
bag_tree(min_n = integer()) %>%
set_engine("C5.0") %>%
set_mode("classification") %>%
translate()## 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 tidymodels.org.
The fit() and fit_xy() arguments have arguments called
case_weights that expect vectors of case weights.