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 `NA`

s. See `model.frame`

for
details.

control

an object of class `Weka_control`

giving
options to be passed to the Weka learner. Available options can be
obtained on-line using the Weka Option Wizard `WOW`

, or
the Weka documentation.

options

a named list of further options, or `NULL`

(default). See **Details**.

A list inheriting from classes `Weka_rules`

and
`Weka_classifiers`

with components including

a reference (of class
`jobjRef`

) to a Java object
obtained by applying the Weka `buildClassifier`

method to build
the specified model using the given control options.

a numeric vector or factor with the model
predictions for the training instances (the results of calling the
Weka `classifyInstance`

method for the built classifier and
each instance).

the matched call.

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.

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.
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.

# NOT RUN { M5Rules(mpg ~ ., data = mtcars) m <- PART(Species ~ ., data = iris) m summary(m) # }