Weka_classifier_meta {RWeka} | R Documentation |
R interfaces to Weka meta learners.
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)
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 NA s. |
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 with an available R/Weka interface (see
list_Weka_interfaces ), can be specified (using the
-W option) via their “base name” as shown
in the interface registry (see the examples). |
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).
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. |
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.
data("iris") ## Use AdaBoostM1 with decision stumps. m1 <- AdaBoostM1(Species ~ ., data = iris, control = c("-W", "DecisionStump")) table(predict(m1), iris$Species)