RWeka (version 0.2-0)

Weka_classifier_trees: R/Weka Classifier Trees

Description

R interfaces to Weka regression and classification tree learners.

Usage

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

control

a character vector with control options, or NULL (default). Available options can be obtained on-line using the Weka Option Wizard WOW, or the Weka documentation.

Value

• A list inheriting from classes Weka_trees 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.

Details

There is a predict method for predicting from the fitted models.

J48 generates unpruned or a pruned C4.5 decision trees (Quinlan, 1993).

LMT implements Logistic Model Trees (Landwehr, 2003).

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.

References

N. Landwehr (2003). Logistic model trees. Masters thesis, Institute for Computer Science, University of Freiburg, Germany. http://www.informatik.uni-freiburg.de/~ml/thesis_landwehr2003.html 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.

Examples

data(iris)
m1 <- J48(Species ~ ., iris)
m1
table(iris$Species, predict(m1))

## 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))

## M5P
DF3 <- read.arff(system.file("arff", "cpu.arff",
                             package = "RWeka"))
m3 <- M5P(class ~ ., data = DF3)
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))