# Weka_classifier_trees

##### 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
`NA`

s. - 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

`M5P`

(where the `P` stands for

##### 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.

##### References

N. Landwehr (2003).
*Logistic model trees*.
Masters thesis, Institute for Computer Science, University of
Freiburg, Germany.
*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))
```

*Documentation reproduced from package RWeka, version 0.1-0, License: GPL version 2 or newer*