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.