RWeka_CBA | R Documentation |
Provides CBA-type classifiers based on RIPPER (Cohen, 1995), C4.5 (Quinlan, 1993) and PART (Frank and Witten, 1998) using the implementation in Weka via RWeka (Hornik et al, 2009).
RIPPER_CBA(formula, data, control = NULL, disc.method = "mdlp") PART_CBA(formula, data, control = NULL, disc.method = "mdlp") C4.5_CBA(formula, data, control = NULL, disc.method = "mdlp")
formula |
A symbolic description of the model to be fitted. Has to be
of form |
data |
A data.frame or a transaction set containing the training data. Data frames are automatically discretized and converted to transactions. |
control |
algorithmic control options for R/Weka Rule learners (see Details Section). |
disc.method |
Discretization method used to discretize continuous
variables if data is a data.frame (default: |
You need to install package RWeka to use these classifiers.
See R/Weka functions JRip
(RIPPER),
J48
(C4.5 rules) PART
for
algorithm details and how control options can be passed on via
control
. An example is given in the Examples Section below.
Memory for RWeka can be increased using the R options (e.g.,
options(java.parameters = "-Xmx1024m")
) before RWeka or
rJava is loaded or any RWeka-based classigier in this package is used.
Returns an object of class CBA.object
representing the
trained classifier.
Michael Hahsler
W. W. Cohen (1995). Fast effective rule induction. In A. Prieditis and S. Russell (eds.), Proceedings of the 12th International Conference on Machine Learning, pages 115-123. Morgan Kaufmann. ISBN 1-55860-377-8.
E. Frank and I. H. Witten (1998). Generating accurate rule sets without global optimization. In J. Shavlik (ed.), Machine Learning: Proceedings of the Fifteenth International Conference. Morgan Kaufmann Publishers: San Francisco, CA.
R. Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.
Hornik K, Buchta C, Zeileis A (2009). "Open-Source Machine Learning: R Meets Weka." Computational Statistics, 24(2), 225-232. doi: 10.1007/s00180-008-0119-7
JRip
(RIPPER), PART
,
CBA.object
.
# You need to install rJava and RWeka ## Not run: data("iris") # learn a classifier using automatic default discretization classifier <- RIPPER_CBA(Species ~ ., data = iris) classifier # inspect the rule base inspect(rules(classifier)) # make predictions for the first few instances of iris predict(classifier, head(iris)) table(predict(classifier, iris), iris$Species) # C4.5 classifier <- C4.5_CBA(Species ~ ., iris) inspect(rules(classifier)) # To use algorithmic options (here for PART), you need to load RWeka library(RWeka) # control options can be found using the Weka Option Wizard (WOW) WOW(PART) # build PART with control option U (Generate unpruned decision list) set to TRUE classifier <- PART_CBA(Species ~ ., data = iris, control = RWeka::Weka_control(U = TRUE)) classifier inspect(rules(classifier)) predict(classifier, head(iris)) ## End(Not run)
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