Weka_classifier_lazy {RWeka} | R Documentation |
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)
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 NA s. |
control |
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. |
options |
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.
A list inheriting from classes Weka_lazy
and
Weka_classifiers
with components including
classifier |
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. |
predictions |
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). |
call |
the matched call. |
D. Aha and D. Kibler (1991). Instance-based learning algorithms. Machine Learning, 6, 37–66.
Z. Zheng & G. Webb, (2000). Lazy learning of Bayesian rules. Machine Learning, 41/1, 53–84.