JRip(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
M5Rules(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
OneR(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
PART(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
NA
s. See model.frame
for
details.Weka_control
giving
options to be passed to the Weka learner. Available options can be
obtained on-line using the Weka Option Wizard WOW
NULL
(default). See Details.Weka_rules
and
Weka_classifiers
with components includingjobjRef
) to a Java object
obtained by applying the Weka buildClassifier
method to build
the specified model using the given control options.classifyInstance
method for the built classifier and
each instance).predict
method for
predicting from the fitted models, and a summary
method based
on evaluate_Weka_classifier
. JRip
implements a propositional rule learner,
M5Rules
generates a decision list for regression problems using
separate-and-conquer. In each iteration it builds an model tree using
M5 and makes the
OneR
builds a simple 1-R classifier, see Holte (1993).
PART
generates PART decision lists using the approach of Frank
and Witten (1998).
The model formulae should only use the + and - operators to indicate the variables to be included or not used, respectively.
Argument options
allows further customization. Currently,
options model
and instances
(or partial matches for
these) are used: if set to TRUE
, the model frame or the
corresponding Weka instances, respectively, are included in the fitted
model object, possibly speeding up subsequent computations on the
object. By default, neither is included.
M. Hall, G. Holmes, and E. Frank (1999).
Generating rule sets from model trees.
Proceedings of the Twelfth Australian Joint Conference on
Artificial Intelligence, Sydney, Australia, pages 1--12.
Springer-Verlag.
R. C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11, 63--91.
I. H. Witten and E. Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques. 2nd Edition, Morgan Kaufmann, San Francisco.
M5Rules(mpg ~ ., data = mtcars)
m <- PART(Species ~ ., data = iris)
m
summary(m)
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