AdaBoostM1(formula, data, subset, na.action, control = Weka_control())
Bagging(formula, data, subset, na.action, control = Weka_control())
LogitBoost(formula, data, subset, na.action, control = Weka_control())
MultiBoostAB(formula, data, subset, na.action, control = Weka_control())
Stacking(formula, data, subset, na.action, control = Weka_control())
NA
s.Weka_control
.
Available options can be obtained on-line using the Weka
Option Wizard WOW
, or the Weka documentation.
Base claWeka_meta
and
Weka_classifiers
with components includingjobjRef
) to a Java object
obtained by applying the Weka buildClassifier
method to build
the specified model using the given control options.classifyInstance
method for the built classifier and
each instance).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
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).
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.
data("iris")
## Use AdaBoostM1 with decision stumps.
m1 <- AdaBoostM1(Species ~ ., data = iris,
control = Weka_control(W = "DecisionStump"))
table(predict(m1), iris$Species)
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