C50::C5.0() fits a model that derives feature rules from a tree for
prediction. A single tree or boosted ensemble can be used. rules::c5_fit()
is a wrapper around this function.
For this engine, there is a single mode: classification
This model has 2 tuning parameters:
trees: # Trees (type: integer, default: 1L)
min_n: Minimal Node Size (type: integer, default: 2L)
Note that C5.0 has a tool for early stopping during boosting where
less iterations of boosting are performed than the number requested.
C5_rules() turns this feature off (although it can be re-enabled using
C50::C5.0Control()).
The rules extension package is required to fit this model.
library(rules)C5_rules(
trees = integer(1),
min_n = integer(1)
) %>%
set_engine("C5.0") %>%
set_mode("classification") %>%
translate()
## C5.0 Model Specification (classification)
##
## Main Arguments:
## trees = integer(1)
## min_n = integer(1)
##
## Computational engine: C5.0
##
## Model fit template:
## rules::c5_fit(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
## trials = integer(1), minCases = integer(1))
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.
Quinlan R (1992). “Learning with Continuous Classes.” Proceedings of the 5th Australian Joint Conference On Artificial Intelligence, pp. 343-348.
Quinlan R (1993).”Combining Instance-Based and Model-Based Learning.” Proceedings of the Tenth International Conference on Machine Learning, pp. 236-243.
Kuhn M and Johnson K (2013). Applied Predictive Modeling. Springer.