RWeka (version 0.2-2)

Weka_classifier_lazy: 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.

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 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).

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.