RWeka (version 0.2-2)

Weka_classifier_meta: R/Weka Meta Learners

Description

R interfaces to Weka meta learners.

Usage

AdaBoostM1(formula, data, subset, na.action, control = NULL)
Bagging(formula, data, subset, na.action, control = NULL)
LogitBoost(formula, data, subset, na.action, control = NULL)
MultiBoostAB(formula, data, subset, na.action, control = NULL)
Stacking(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. Base classifiers wit

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.

Details

There is a predict method for predicting from the fitted models.

AdaBoostM1 implements the Adaboost M1 method of Freund and Schapire (1996).

Bagging provides bagging (Breiman, 1996).

LogitBoost perfoms 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).

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 Eibe 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.

Examples

data("iris")
## Use AdaBoostM1 with decision stumps.
m1 <- AdaBoostM1(Species ~ ., data = iris,
                 control = c("-W", "DecisionStump"))
table(predict(m1), iris$Species)