# Data Mining Algorithms In R/Packages/RWeka/Weka classifier meta

## Contents

## Description[edit]

R interfaces to Weka meta learners.

## Usage[edit]

*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[edit]

*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*, an object of class Weka_control giving options to be passed to the Weka learner.

*options*, a named list of further options, or NULL (default).

## Details[edit]

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[edit]

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.

## Example[edit]

m1 <- AdaBoostM1(Species ~ ., data = iris, control = Weka_control(W = "DecisionStump")) table(predict(m1), iris$Species) summary(m1) # uses evaluate_Weka_classifier() m2 <- AdaBoostM1(Species ~ ., data = iris, control = Weka_control(W = list(J48, M = 30)))