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)
NA
s.Weka_control
giving
options to be passed to the Weka learner. Available options can be
obtained on-line using the Weka Option Wizard WOW
NULL
(default). See Details.Weka_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, 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.
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.
## 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)))
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