R/Weka Lazy Learners
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
- an object of class
Weka_controlgiving options to be passed to the Weka learner. Available options can be obtained on-line using the Weka Option Wizard
- a named list of further options, or
NULL(default). See Details.
IBk provides a $k$-nearest neighbors classifier, see Aha &
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.
- A list inheriting from classes
Weka_classifierswith components including
classifier a reference (of class
jobjRef) to a Java object obtained by applying the Weka
buildClassifiermethod 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
classifyInstancemethod for the built classifier and each instance).
call the matched call.
LBR requires Weka package
D. Aha and D. Kibler (1991). Instance-based learning algorithms. Machine Learning, 6, 37--66. Z. Zheng and G. Webb (2000). Lazy learning of Bayesian rules. Machine Learning, 41/1, 53--84.