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 = NULL)
LBR(formula, data, subset, na.action, control = 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.
control a character vector with control options, or NULL (default). Available options can be obtained on-line using the Weka Option Wizard WOW, or the Weka documentation.

Details

There is a predict method for predicting from the fitted models.

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).

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.

References

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.


[Package RWeka version 0.2-2 Index]