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 `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.

### 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.

### 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 and G. Webb (2000). Lazy learning of Bayesian rules. Machine Learning, 41/1, 53–84.

### See Also

Weka_classifiers

[Package RWeka version 0.4-1 Index]