Weka_classifier_lazy {RWeka} | R Documentation |
R/Weka Lazy Learners
Description
R interfaces to Weka lazy learners.
Usage
IBk(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
LBR(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
Arguments
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 |
Details
There are a predict
method for
predicting from the fitted models, and a summary
method based
on evaluate_Weka_classifier
.
IBk
provides a -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.
Value
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. |
Note
LBR
requires Weka package lazyBayesianRules to be
installed.
References
D. Aha and D. Kibler (1991). Instance-based 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.