R interfaces to Weka lazy learners.
IBk(formula, data, subset, na.action, control = Weka_control(), options = NULL) LBR(formula, data, subset, na.action, control = Weka_control(), options = NULL)
a symbolic description of the model to be fit.
an optional data frame containing the variables in the model.
an optional vector specifying a subset of observations to be used in the fitting process.
a function which indicates what should happen when
the data contain
a named list of further options, or
(default). See Details.
A list inheriting from classes
Weka_classifiers with components including
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.
a numeric vector or factor with the model
predictions for the training instances (the results of calling the
classifyInstance method for the built classifier and
the matched call.
IBk provides a \(k\)-nearest neighbors classifier, see Aha &
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.
options allows further customization. Currently,
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.
D. Aha and D. Kibler (1991). Instance-based learning algorithms. Machine Learning, 6, 37--66. 10.1007/BF00153759.
Z. Zheng and G. Webb (2000). Lazy learning of Bayesian rules. Machine Learning, 41/1, 53--84. 10.1023/A:1007613203719.