IBk(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
LBR(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
NA
s. See model.frame
for
details.Weka_control
giving
options to be passed to the Weka learner. Available options can be
obtained on-line using the Weka Option Wizard WOW
, or
the Weka documentation.NULL
(default). See Details.Weka_lazy
and
Weka_classifiers
with components including
jobjRef
) 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
.IBk
provides a \(k\)-nearest neighbors classifier, see Aha &
Kibler (1991).
LBR
(“Lazy Bayesian Rules”) implements a lazy learning
approach to lessening the attribute-independence assumption of naive
Bayes as suggested by Zheng & Webb (2000).
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.
Z. Zheng and G. Webb (2000). Lazy learning of Bayesian rules. Machine Learning, 41/1, 53--84.