C5.0 rule-based classification modelsSource:
C5_rules() defines a model that derives feature rules from a tree for
prediction. A single tree or boosted ensemble can be used. This function can
fit classification models.
There are different ways to fit this model, and the method of estimation is chosen by setting the model engine. The engine-specific pages for this model are listed below.
More information on how parsnip is used for modeling is at https://www.tidymodels.org/.
A single character string for the type of model. The only possible value for this model is "classification".
A non-negative integer (no greater than 100) for the number of members of the ensemble.
An integer greater between zero and nine for the minimum number of data points in a node that are required for the node to be split further.
A single character string specifying what computational engine to use for fitting.
C5.0 is a classification model that is an extension of the C4.5
model of Quinlan (1993). It has tree- and rule-based versions that also
include boosting capabilities.
C5_rules() enables the version of the model
that uses a series of rules (see the examples below). To make a set of
rules, an initial C5.0 tree is created and flattened into rules. The rules
are pruned, simplified, and ordered. Rule sets are created within each
iteration of boosting.
This function only defines what type of model is being fit. Once an engine
is specified, the method to fit the model is also defined. See
set_engine() for more on setting the engine, including how to set engine
The model is not trained or fit until the
fit() function is used
with the data.
value <- 1 C5_rules(argument = !!value)
Quinlan R (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers.
show_engines("C5_rules") #> # A tibble: 0 × 2 #> # ℹ 2 variables: engine <chr>, mode <chr> C5_rules() #> ! parsnip could not locate an implementation for `C5_rules` model #> specifications. #> ℹ The parsnip extension package rules implements support for this #> specification. #> ℹ Please install (if needed) and load to continue. #> C5.0 Model Specification (classification) #> #> Computational engine: C5.0 #>