Weka_classifier_trees: R/Weka Classifier Trees

In RWeka: R/Weka Interface

R/Weka Classifier Trees

Description

R interfaces to Weka regression and classification tree learners.

Usage

J48(formula, data, subset, na.action,
    control = Weka_control(), options = NULL)
LMT(formula, data, subset, na.action,
    control = Weka_control(), options = NULL)
M5P(formula, data, subset, na.action,
    control = Weka_control(), options = NULL)
DecisionStump(formula, data, subset, na.action,
              control = Weka_control(), options = NULL)

parse_Weka_digraph(x, plainleaf = TRUE)

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 NAs. 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.
x	a character string with the Weka digraph of a Weka classifier tree (see Details).
plainleaf	a logical indicating whether the leaves should be kept ‘plain’, i.e., with empty labels.

Details

There are a predict method for predicting from the fitted models, and a summary method based on evaluate_Weka_classifier.

There is also a plot method for fitted binary Weka_trees via the facilities provided by package partykit. This converts the Weka_tree to a party object and then simply calls the plot method of this class (see plot.party).

Provided the Weka classification tree learner implements the “Drawable” interface (i.e., provides a graph method), write_to_dot can be used to create a DOT representation of the tree for visualization via Graphviz or the Rgraphviz package.

J48 generates unpruned or pruned C4.5 decision trees \bibcitepQuinlan:1993.

LMT implements “Logistic Model Trees” \bibcitepLandwehr:2003, Landwehr+Hall+Frank:2005.

M5P (where the ‘⁠P⁠’ stands for ‘prime’) generates M5 model trees using the M5' algorithm, which was introduced in \bibcitetWang+Witten:1997 and enhances the original M5 algorithm by \bibcitetQuinlan:1992.

DecisionStump implements decision stumps (trees with a single split only), which are frequently used as base learners for meta learners such as Boosting.

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.

parse_Weka_digraph can parse the graph associated with a Weka tree classifier (and obtained by invoking its graph() method in Weka), returning a simple list with nodes and edges.

Value

A list inheriting from classes Weka_tree 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

\bibshow

Landwehr:2003, Landwehr+Hall+Frank:2005, Quinlan:1992, Quinlan:1993, Wang+Witten:1997, Witten+Frank:2005

See Also

Weka_classifiers

Examples

m1 <- J48(Species ~ ., data = iris)

## print and summary
m1
summary(m1) # calls evaluate_Weka_classifier()
table(iris$Species, predict(m1)) # by hand

## visualization
## use partykit package
if(require("partykit", quietly = TRUE)) plot(m1)
## or Graphviz
write_to_dot(m1)
## or Rgraphviz
## Not run: 
library("Rgraphviz")
ff <- tempfile()
write_to_dot(m1, ff)
plot(agread(ff))

## End(Not run)
## The first uses the Weka digraph for the classifier tree
di <- rJava::.jcall(m1$classifier, "S", "graph")
writeLines(di)
## for which we can extract the information about nodes and edges via
parse_Weka_digraph(di)

## Using some Weka data sets ...

## J48
DF2 <- read.arff(system.file("arff", "contact-lenses.arff",
                             package = "RWeka"))
m2 <- J48(`contact-lenses` ~ ., data = DF2)
m2
table(DF2$`contact-lenses`, predict(m2))
if(require("partykit", quietly = TRUE)) plot(m2)

## M5P
DF3 <- read.arff(system.file("arff", "cpu.arff", package = "RWeka"))
m3 <- M5P(class ~ ., data = DF3)
m3
if(require("partykit", quietly = TRUE)) plot(m3)

## Logistic Model Tree.
DF4 <- read.arff(system.file("arff", "weather.arff", package = "RWeka"))
m4 <- LMT(play ~ ., data = DF4)
m4
table(DF4$play, predict(m4))

## Larger scale example.
if(require("mlbench", quietly = TRUE)
   && require("partykit", quietly = TRUE)) {
    ## Predict diabetes status for Pima Indian women
    data("PimaIndiansDiabetes", package = "mlbench")
    ## Fit J48 tree with reduced error pruning
    m5 <- J48(diabetes ~ ., data = PimaIndiansDiabetes,
              control = Weka_control(R = TRUE))
    plot(m5)
    ## (Make sure that the plotting device is big enough for the tree.)
}

RWeka documentation built on July 13, 2026, 5:08 p.m.