R/Weka Rule Learners
R interfaces to Weka rule learners.
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)formula |
a symbolic description of the model to be fit. |
data |
an optional data frame containing the variables in the model. |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
na.action |
a function which indicates what should happen when
the data contain |
control |
an object of class |
options |
a named list of further options, or |
There are a predict method for
predicting from the fitted models, and a summary method based
on evaluate_Weka_classifier.
JRip implements a propositional rule learner, “Repeated
Incremental Pruning to Produce Error Reduction” (RIPPER), as proposed
by Cohen (1995).
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 “best” leaf into a rule. See Hall, Holmes and
Frank (1999) for more information.
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.
A list inheriting from classes Weka_rules and
Weka_classifiers with components including
classifier |
a reference (of class
|
predictions |
a numeric vector or factor with the model
predictions for the training instances (the results of calling the
Weka |
call |
the matched call. |
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. http://citeseer.ist.psu.edu/cohen95fast.html
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. http://www.cs.waikato.ac.nz/~eibe/pubs/ML98-57.ps.gz
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. http://citeseer.ist.psu.edu/holmes99generating.html
R. C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11, 63–91. doi: 10.1023/A:1022631118932.
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)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.