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 list inheriting from classes Weka_lazy
and
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
Weka classifyInstance
method for the built classifier and
each instance).
the matched call.
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 NA
s. See model.frame
for
details.
an object of class 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.
a named list of further options, or NULL
(default). See Details.
There are a 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.
D. Aha and D. Kibler (1991). Instance-based learning algorithms. Machine Learning, 6, 37--66. tools:::Rd_expr_doi("10.1007/BF00153759").
Z. Zheng and G. Webb (2000). Lazy learning of Bayesian rules. Machine Learning, 41/1, 53--84. tools:::Rd_expr_doi("10.1023/A:1007613203719").
Weka_classifiers