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