# Data Mining Algorithms In R/Packages/RWeka/Weka classifier rules

## Description

editR interfaces to Weka rule learners.

## Usage

edit*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

edit*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 NAs.

*control*, an object of class Weka_control giving options to be passed to the Weka learner.

*options*, a named list of further options, or NULL (default).

## Details

editThere 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.

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.

## Value

editA 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.

## Example

editM5Rules(mpg ~ ., data = mtcars) m <- PART(Species ~ ., data = iris) summary(m)