Weka_classifier_trees

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R/Weka Classifier Trees

R interfaces to Weka regression and classification tree learners.

Keywords
models, regression, classif, tree
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)
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.
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).

Value

  • A list inheriting from classes Weka_trees and Weka_classifiers with components including
  • classifiera 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.
  • predictionsa 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).
  • callthe matched call.

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.

Aliases
  • J48
  • LMT
  • M5P
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))
Documentation reproduced from package RWeka, version 0.1-0, License: GPL version 2 or newer

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