RWeka (version 0.4-43)

Weka_classifier_trees: R/Weka Classifier Trees


R interfaces to Weka regression and classification tree learners.


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)



a symbolic description of the model to be fit.


an optional data frame containing the variables in the model.


an optional vector specifying a subset of observations to be used in the fitting process.


a function which indicates what should happen when the data contain NAs. See model.frame for details.


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.


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


A list inheriting from classes Weka_tree 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.

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

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 (Quinlan, 1993).

LMT implements “Logistic Model Trees” (Landwehr, 2003; Landwehr et al., 2005).

M5P (where the P stands for ‘prime’) generates M5 model trees using the M5' algorithm, which was introduced in Wang & Witten (1997) and enhances the original M5 algorithm by Quinlan (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.


N. Landwehr (2003). Logistic Model Trees. Master's thesis, Institute for Computer Science, University of Freiburg, Germany.

N. Landwehr, M. Hall, and E. Frank (2005). Logistic Model Trees. Machine Learning, 59, 161--205. 10.1007/s10994-005-0466-3.

R. Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.

R. Quinlan (1992). Learning with continuous classes. Proceedings of the Australian Joint Conference on Artificial Intelligence, 343--348. World Scientific, Singapore.

Y. Wang and I. H. Witten (1997). Induction of model trees for predicting continuous classes. Proceedings of the European Conference on Machine Learning. University of Economics, Faculty of Informatics and Statistics, Prague.

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

See Also



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

## print and summary
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
## or Rgraphviz
# }
ff <- tempfile()
write_to_dot(m1, ff)
# }
## Using some Weka data sets ...

## J48
DF2 <- read.arff(system.file("arff", "contact-lenses.arff",
                             package = "RWeka"))
m2 <- J48(`contact-lenses` ~ ., data = DF2)
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)
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)
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))
    ## (Make sure that the plotting device is big enough for the tree.)
# }