# Weka_classifier_meta

##### R/Weka Meta Learners

R interfaces to Weka meta learners.

- Keywords
- models, regression, classif

##### Usage

```
AdaBoostM1(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
Bagging(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
LogitBoost(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
MultiBoostAB(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
Stacking(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
CostSensitiveClassifier(formula, data, subset, na.action,
control = Weka_control(), options = 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. 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`

- options
- a named list of further options, or
`NULL`

(default). See**Details**.

##### Details

There are a `predict`

method for
predicting from the fitted models, and a `summary`

method based
on `evaluate_Weka_classifier`

.

`AdaBoostM1`

implements the AdaBoost M1 method of Freund and
Schapire (1996).

`Bagging`

provides bagging (Breiman, 1996).

`LogitBoost`

performs boosting via additive logistic regression
(Friedman, Hastie and Tibshirani, 2000).

`MultiBoostAB`

implements MultiBoosting (Webb, 2000), an
extension to the AdaBoost technique for forming decision
committees which can be viewed as a combination of AdaBoost and

`Stacking`

provides stacking (Wolpert, 1992).

`CostSensitiveClassifier`

makes its base classifier
cost-sensitive.

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.

##### Value

- A list inheriting from classes
`Weka_meta`

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.

##### Note

`multiBoostAB`

requires Weka package

##### References

L. Breiman (1996).
Bagging predictors.
*Machine Learning*, **24**/2, 123--140.

Y. Freund and R. E. Schapire (1996).
Experiments with a new boosting algorithm.
In *Proceedings of the International Conference on Machine
Learning*, pages 148--156.
Morgan Kaufmann: San Francisco.

J. H. Friedman, T. Hastie, and R. Tibshirani (2000).
Additive logistic regression: A statistical view of boosting.
*Annals of Statistics*, **28**/2, 337--374.

G. I. Webb (2000).
MultiBoosting: A technique for combining boosting and wagging.
*Machine Learning*, **40**/2, 159--196.

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

D. H. Wolpert (1992).
Stacked generalization.
*Neural Networks*, **5**, 241--259.

##### See Also

##### Examples

```
## Use AdaBoostM1 with decision stumps.
m1 <- AdaBoostM1(Species ~ ., data = iris,
control = Weka_control(W = "DecisionStump"))
table(predict(m1), iris$Species)
summary(m1) # uses evaluate_Weka_classifier()
## Control options for the base classifiers employed by the meta
## learners (apart from Stacking) can be given as follows:
m2 <- AdaBoostM1(Species ~ ., data = iris,
control = Weka_control(W = list(J48, M = 30)))
```

*Documentation reproduced from package RWeka, version 0.4-18, License: GPL-2*