Description Usage Arguments Details Value Note References See Also
R interfaces to Weka lazy learners.
1 2 3 4 
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 
control 
an object of class 
options 
a named list of further options, or 
There are a predict
method for
predicting from the fitted models, and a summary
method based
on evaluate_Weka_classifier
.
IBk
provides a knearest neighbors classifier, see Aha &
Kibler (1991).
LBR
(“Lazy Bayesian Rules”) implements a lazy learning
approach to lessening the attributeindependence 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.
A list inheriting from classes Weka_lazy
and
Weka_classifiers
with components including
classifier 
a reference (of class

predictions 
a numeric vector or factor with the model
predictions for the training instances (the results of calling the
Weka 
call 
the matched call. 
LBR
requires Weka package lazyBayesianRules to be
installed.
D. Aha and D. Kibler (1991). Instancebased learning algorithms. Machine Learning, 6, 37–66. doi: 10.1007/BF00153759.
Z. Zheng and G. Webb (2000). Lazy learning of Bayesian rules. Machine Learning, 41/1, 53–84. doi: 10.1023/A:1007613203719.
Weka_classifiers
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