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