For this engine, there is a single mode: classification
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.
boost_tree(trees = integer(), min_n = integer(), sample_size = numeric()) %>%
set_engine("C5.0") %>%
set_mode("classification") %>%
translate()
## 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.
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.
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.
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.
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