Weka_classifier_meta {RWeka} | R Documentation

## R/Weka Meta Learners

### Description

R interfaces to Weka meta learners.

### 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.
`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`, or the Weka documentation. Base classifiers with an available R/Weka interface (see `list_Weka_interfaces`), can be specified (using the W option) via their “base name” as shown in the interface registry (see the examples), or their interface function.
`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 “wagging”.

`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.

### 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

Weka_classifiers

### 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)))

[Package RWeka version 0.4-1 Index]