Weka_classifier_lazy: 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 NAs. See model.frame for details.

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.

Value

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.

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.

References

D. Aha and D. Kibler (1991). Instance-based learning algorithms. Machine Learning, 6, 37--66. 10.1007/BF00153759.