Weka_classifier_rules {RWeka} | R Documentation

## R/Weka Rule Learners

### Description

R interfaces to Weka rule learners.

### 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.
`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

`classifier` | 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.
`predictions` | 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).
`call` | 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

Weka_classifiers

### Examples

    M5Rules(mpg ~ ., data = mtcars)

    m <- PART(Species ~ ., data = iris)
    m
    summary(m)

[Package RWeka version 0.4-1 Index]