For this engine, there is a single mode: classification
This model has 1 tuning parameters:
min_n
: Minimal Node Size (type: integer, default: 2L)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")
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.
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.
Breiman, L. 1996. "Bagging predictors". Machine Learning. 24 (2): 123-140
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.
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