# Weka_classifier_rules

##### R/Weka Rule Learners

R interfaces to Weka rule learners.

- Keywords
- models, regression, classif

##### Usage

```
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)
```

##### Arguments

- 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**.

##### Details

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.

##### Value

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.

##### References

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.

I. H. Witten and E. Frank (2005).
*Data Mining: Practical Machine Learning Tools and Techniques*.
2nd Edition, Morgan Kaufmann, San Francisco.

##### See Also

##### Examples

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

*Documentation reproduced from package RWeka, version 0.4-40, License: GPL-2*